DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because in Fig. 1 and 6-7, “nerve radiation field” should read “neural radiance field”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13 recites the limitation "the first type of roaming point location" in line 1. There is insufficient antecedent basis for this limitation in the claim. For the sake of examination, claim 13 is interpreted to be dependent on claim 11, instead of claim 12.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8-13, and 15-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Montero et al. (US 20250166311 A1), hereinafter Montero.
Regarding claim 1, Montero teaches a three-dimensional scene reconstruction method (Paragraph 0083 – “FIG. 1 depicts a block diagram of an example virtual walkthrough video generation system 10 according to example embodiments of the present disclosure…the virtual walkthrough video generation system 10 can include a neural radiance field model 12 that is operable to generate view synthesis renderings 16 of the environment based on a learned three-dimensional representation of the environment”; Note: the process performed by the system is equivalent to a 3D scene reconstruction method), comprising:
constructing a neural radiance field for a three-dimensional scene based on a multi-view image sequence in the three-dimensional scene (Paragraph 0084-0085 – “the virtual walkthrough video generation system 10 can obtain a neural radiance field model 12 associated with an environment. The neural radiance field model 12 may have been trained on a plurality of images of the environment…The neural radiance field model 12 can process a plurality of positions 14 associated with a path to generate a plurality of view synthesis renderings 16 of the environment. The plurality of positions 14 can be associated with a plurality of locations within the environment. The path can be associated with a route through the environment. The path can be manually determined, can be based on image-capture positions, and/or may be automatically determined based on regions of interest in the environment, locations of entrance and exit, and/or based on determined pathways”; Note: a neural radiance field model is generated based on a plurality of image-captured positions of a path, which is equivalent to the multi-view image sequence. The environment is equivalent to the 3D scene; also see fig. 20);
for each given point location center, determining ray sampling points at the point location center and color information of the ray sampling points based on the neural radiance field (Paragraph 0125, 0152, 0303 – “The NeRF prediction system (a) depicted in FIG. 6A can sample points x along rays that are traced from the camera center of projection 602 through each pixel, then encodes those points with a positional encoding (PE) γ to produce a feature γ(x)… A two-dimensional view direction and a three-dimensional position can be processed with the neural radiance field model 904 to generate prediction data 906. The prediction data 906 can include one or more predicted density values and/or one or more predicted color values…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: ray sampling points and color information of those points are determined. The camera center of projection is equivalent to the given point location center);
performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center (Paragraph 0121-0122, 0303 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour. The rendering can include rendering a three-dimensional model 564. The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data…the neural radiance field model can be utilized to learn depth values and color values for an environment. The learned color representations 572 and the learned depth representations 574 can be utilized to generate view synthesis renderings 570 of the environment…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: the color information of the sampled points of the ray are rendered into multi-sphere image 562. The rendering is multi-layered since multiple spheres are rendered around a position).
Regarding claim 8, Montero teaches the method of claim 1. Montero further teaches wherein, for each given point location center, the determining ray sampling points at the point location center and color information of the ray sampling points based on the neural radiance field, comprises: for each given point location center, determining the radiation light ray and the ray sampling points on the radiation light ray at the point location center according to the neural radiance field (Paragraph 0125 – “The NeRF prediction system (a) depicted in FIG. 6A can sample points x along rays that are traced from the camera center of projection 602 through each pixel, then encodes those points with a positional encoding (PE) γ to produce a feature γ(x)”; Note: rays and ray sampling points are determined. The camera center of projection is equivalent to the given point location center, and the rays are equivalent to the radiation light ray); determining the color information of the ray sampling point according to the neural radiance field (Paragraph 0152, 0303 – “A two-dimensional view direction and a three-dimensional position can be processed with the neural radiance field model 904 to generate prediction data 906. The prediction data 906 can include one or more predicted density values and/or one or more predicted color values…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: color information of the sampled points are determined).
Regarding claim 9, Montero teaches the method of claim 1. Montero further teaches wherein, the method further comprises: acquiring information about roaming pose of the user, wherein the information about roaming pose includes the roaming position and roaming posture (Paragraph 0118, 0121, 0169 – “The video player system can include one or more user interface elements for providing navigational inputs for controlling the virtual walkthrough. The navigational adjustments can include changing the movement direction, changing the movement speed, changing the view direction, and/or changing the zoom. The video player system may include one or more joystick user interface elements 512. The one or more joystick user interface elements 512 can be utilized to change the movement direction, view direction, speed, and/or zoom…The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed…At operation 1510, the method includes server computing system 300 receiving a request for an immersive view of a location. For example, the request for the immersive view may be associated with temporal conditions (e.g., an immersive view of the location at a particular time, including a time of day, time of year, etc.) and/or other conditions including lighting conditions, weather conditions, and the like”; Note: the user requests a view of a location. The position being viewed is equivalent to the roaming position, and the view direction and zoom is equivalent to the roaming posture); if the roaming position is within the point location, displaying a corresponding roaming image according to the information about roaming pose and the multi-sphere image reconstructed at the point location center (Paragraph 0121, 0170 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed…At operation 1520, the server computing system 300 may obtain a 3D scene associated with the location. For example, the server computing system may obtain the 3D scene from 3D scene imagery. For example, the 3D scene of the location obtained from 3D scene imagery may be a 3D scene which corresponds to, or is roughly associated with, the conditions of the request”; Note: the sphere associated with the position being viewed is rendered/displayed. The position being viewed is equivalent to the roaming pose information. It is implied that the roaming position is within the point location since the sphere encompasses the position of the user (position being viewed)).
Regarding claim 10, Montero teaches the method of claim 1. Montero further teaches wherein, based on roaming position information of a user in a virtual scene approximated by the multi-sphere image and target virtual scene data, a target roaming image is obtained for displaying (Paragraph 0085-0086, 0121 – “The neural radiance field model 12 can process a plurality of positions 14 associated with a path to generate a plurality of view synthesis renderings 16 of the environment. The plurality of positions 14 can be associated with a plurality of locations within the environment. The path can be associated with a route through the environment…The example virtual walkthrough video generation system 10 can process the plurality of view synthesis renderings 16 to generate a virtual walkthrough video 18…The virtual walkthrough video 18 may then be obtained and utilized by a virtual walkthrough interface 18 to provide virtual tours to users…The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour”; Note: the positions and path information are equivalent to the roaming position information, the virtual walkthrough path and environment is equivalent to the target virtual scene data, and the view synthesis rendering within the sphere is equivalent to the target roaming image), wherein the target virtual scene data is determined based on the roaming position information and virtual scene data, and the virtual scene data comprises the constructed neural radiance field (Paragraph 0085-0086 – “The neural radiance field model 12 can process a plurality of positions 14 associated with a path to generate a plurality of view synthesis renderings 16 of the environment. The plurality of positions 14 can be associated with a plurality of locations within the environment. The path can be associated with a route through the environment…The example virtual walkthrough video generation system 10 can process the plurality of view synthesis renderings 16 to generate a virtual walkthrough video 18…The virtual walkthrough video 18 may then be obtained and utilized by a virtual walkthrough interface 18 to provide virtual tours to users”; Note: the positions and path information are equivalent to the roaming position information, and the virtual walkthrough path and environment is equivalent to the target virtual scene data. The virtual walkthrough path and environment are determined by the neural radiance field model’s processing of the position and path information).
Regarding claim 11, Montero teaches the method of claim 10. Montero further teaches wherein, the virtual scene includes at least one first type of roaming point location, the roaming position information is located at the first type of roaming point location (Paragraph 0121 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour. The rendering can include rendering a three-dimensional model 564. The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data”; Note: the rendering within the sphere is equivalent to the first type of roaming point location, and the virtual walkthrough/tour is located within the sphere), and the target virtual scene data includes the multi-sphere data corresponding to the current first type of roaming point location (Paragraph 0121 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour. The rendering can include rendering a three-dimensional model 564. The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data”; Note: the spheres are equivalent to the multi-sphere data corresponding to the first type).
Regarding claim 12, Montero teaches the method of claim 10. Montero further teaches wherein, the target virtual scene data further comprises grid data corresponding to the roaming position information (Paragraph 0121 – “For some environments (e.g., large outdoor environments), the scenes and objects outside of the sphere 562 may be rendered via a different technique (e.g., to conserve computational resources). For example, neural radiance field model rendering can be performed within the sphere 562 and mesh-based image projection (e.g., three-dimensional meshes for the environment can be determined, images of the environment can be segmented, and the segmented images can be projected on the three-dimensional meshes to generate three-dimensional models of the environment) can be performed outside of the sphere 562”; Note: the meshes are equivalent to the grid data).
Regarding claim 13, Montero teaches the method of claim 11. Montero further teaches wherein, the first type of roaming point location comprises a central area and a boundary area (Fig. 5F, Paragraph 0124 – “The rendering sphere system 590 can include neural radiance field model rendering within a first distance 592 and a second distance 594 around the user”; Note: Fig. 5F shows the central area 592 and the boundary area 594. See screenshot below), and in response to the roaming position information being located in the central area of the first type of roaming point location, the target virtual scene data includes multi-sphere data corresponding to the current first type of roaming point location (Paragraph 0121, 0124 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed…The rendering sphere system 590 can include neural radiance field model rendering within a first distance 592 and a second distance 594 around the user. The other portions of the environment 596 within view of the position but outside of the second distance 594 can be rendered via image projection”; Note: the space in the central area 592 is rendered as multi-sphere data. The sphere is a first type of roaming point location); in response to the roaming position information being located in the boundary area of the first type of roaming point location, the target virtual scene data includes multi-sphere data corresponding to the current first type of roaming point location and grid data corresponding to the roaming position information (Paragraph 0121, 0124 – “neural radiance field model rendering can be performed within the sphere 562 and mesh-based image projection (e.g., three-dimensional meshes for the environment can be determined, images of the environment can be segmented, and the segmented images can be projected on the three-dimensional meshes to generate three-dimensional models of the environment) can be performed outside of the sphere 562…The neural radiance field model renderings within the second distance 594 but outside of the first distance 592 may be blended with the image projection rendering to provide a smooth transition between the two rendering types”; Note: the space in the boundary area 594 is rendered as a mix of multi-sphere data and mesh/grid data. The sphere is a first type of roaming point location).
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Screenshot of Fig. 5F (taken from Montero)
Regarding claim 15, Montero teaches the method of claim 10. Montero further teaches wherein, the virtual scene further comprises at least one second type of roaming point location, the roaming position information is located at the second type of roaming point location (Paragraph 0121 – “For some environments (e.g., large outdoor environments), the scenes and objects outside of the sphere 562 may be rendered via a different technique (e.g., to conserve computational resources). For example, neural radiance field model rendering can be performed within the sphere 562 and mesh-based image projection (e.g., three-dimensional meshes for the environment can be determined, images of the environment can be segmented, and the segmented images can be projected on the three-dimensional meshes to generate three-dimensional models of the environment) can be performed outside of the sphere 562”; Note: the outside of the sphere is the second type of roaming point location), and the target virtual scene data includes grid data corresponding to the roaming position information (Paragraph 0121 – “For some environments (e.g., large outdoor environments), the scenes and objects outside of the sphere 562 may be rendered via a different technique (e.g., to conserve computational resources). For example, neural radiance field model rendering can be performed within the sphere 562 and mesh-based image projection (e.g., three-dimensional meshes for the environment can be determined, images of the environment can be segmented, and the segmented images can be projected on the three-dimensional meshes to generate three-dimensional models of the environment) can be performed outside of the sphere 562”; Note: the meshes are equivalent to the grid data).
Regarding claim 16, Montero teaches the method of claim 13. Montero further teaches wherein, in response to the target virtual scene data including sphere data corresponding to the current first type of roaming point location and grid data corresponding to the roaming position information, the multi-sphere data corresponding to the current first type of roaming point location and the grid data corresponding to the roaming position information is mixedly rendered, to obtain the target roaming image (Paragraph 0121, 0124 – “For some environments (e.g., large outdoor environments), the scenes and objects outside of the sphere 562 may be rendered via a different technique (e.g., to conserve computational resources). For example, neural radiance field model rendering can be performed within the sphere 562 and mesh-based image projection (e.g., three-dimensional meshes for the environment can be determined, images of the environment can be segmented, and the segmented images can be projected on the three-dimensional meshes to generate three-dimensional models of the environment) can be performed outside of the sphere 562… The neural radiance field model renderings within the second distance 594 but outside of the first distance 592 may be blended with the image projection rendering to provide a smooth transition between the two rendering types”; Note: the spheres and meshes (grid data) are rendered together. The output neural radiance field model rendering is equivalent to the target roaming image).
Regarding claim 17, Montero teaches the method of claim 10. Montero further teaches wherein, in response to a selection operation of any new roaming point location, the user is controlled to jump from the current roaming point location to the new roaming point location (Paragraph 0118 – “The video player system can include one or more user interface elements for providing navigational inputs for controlling the virtual walkthrough. The navigational adjustments can include changing the movement direction, changing the movement speed, changing the view direction, and/or changing the zoom. The video player system may include one or more joystick user interface elements 512. The one or more joystick user interface elements 512 can be utilized to change the movement direction, view direction, speed, and/or zoom. For example, the user may pull a joystick user interface element down to change the movement direction. In response to receiving the input, the particular frame 506 being depicted can be determined. A corresponding frame 514 in the second portion 510 can then be determined. The playback can then jump to the corresponding frame 514”; Note: the user can change movement direction, which results in a selection of a new roaming point location. The user display then jumps from the current frame/location to the new frame/location), and the roaming image corresponding to the current roaming point location is switched to the roaming image corresponding to the new roaming point location (Paragraph 0118 – “the user may pull a joystick user interface element down to change the movement direction. In response to receiving the input, the particular frame 506 being depicted can be determined. A corresponding frame 514 in the second portion 510 can then be determined. The playback can then jump to the corresponding frame 514”; Note: the current frame, which is equivalent to the roaming image, is switched/jumped to the new roaming location); wherein, the current roaming point location is a first type of roaming point location or a second type of roaming point location (Paragraph 0121 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour”; Note: the roaming point location is a first type, within a sphere), and the new roaming point location is another first type of roaming point locations or another second type of roaming point locations other than the current roaming point location (Paragraph 0118, 0121 – “The one or more joystick user interface elements 512 can be utilized to change the movement direction, view direction, speed, and/or zoom. For example, the user may pull a joystick user interface element down to change the movement direction…The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour”; Note: the current roaming point location is a first type, within a sphere. The sphere moves with the tour, based on user input, and thus, the new roaming location is also a first type within the sphere).
Regarding claim 18, Montero teaches an electronic device (Paragraph 0214 – “The user computing system 102 can include any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop)”), comprising:
a processor; and a memory for storing executable instructions of the processor; wherein the executable instructions, when executed by the processor (Paragraph 0215 – “The user computing system 102 includes one or more processors 112 and a memory 114…The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing system 102 to perform operations”), cause the processor to implement:
constructing a neural radiance field for a three-dimensional scene based on a multi-view image sequence in the three-dimensional scene (Paragraph 0084-0085 – “the virtual walkthrough video generation system 10 can obtain a neural radiance field model 12 associated with an environment. The neural radiance field model 12 may have been trained on a plurality of images of the environment…The neural radiance field model 12 can process a plurality of positions 14 associated with a path to generate a plurality of view synthesis renderings 16 of the environment. The plurality of positions 14 can be associated with a plurality of locations within the environment. The path can be associated with a route through the environment. The path can be manually determined, can be based on image-capture positions, and/or may be automatically determined based on regions of interest in the environment, locations of entrance and exit, and/or based on determined pathways”; Note: a neural radiance field model is generated based on a plurality of image-captured positions of a path, which is equivalent to the multi-view image sequence. The environment is equivalent to the 3D scene);
for each given point location center, determining ray sampling points at the point location center and color information of the ray sampling points based on the neural radiance field (Paragraph 0125, 0152, 0303 – “The NeRF prediction system (a) depicted in FIG. 6A can sample points x along rays that are traced from the camera center of projection 602 through each pixel, then encodes those points with a positional encoding (PE) γ to produce a feature γ(x)… A two-dimensional view direction and a three-dimensional position can be processed with the neural radiance field model 904 to generate prediction data 906. The prediction data 906 can include one or more predicted density values and/or one or more predicted color values…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: ray sampling points and color information of those points are determined. The camera center of projection is equivalent to the given point location center);
performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center (Paragraph 0121-0122, 0303 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour. The rendering can include rendering a three-dimensional model 564. The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data…the neural radiance field model can be utilized to learn depth values and color values for an environment. The learned color representations 572 and the learned depth representations 574 can be utilized to generate view synthesis renderings 570 of the environment…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: the color information of the sampled points of the ray are rendered into multi-sphere image 562. The rendering is multi-layered since multiple spheres are rendered around a position).
Regarding claim 19, Montero teaches a non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor (Paragraph 0011 – “The system can include one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations”), causes the processor to implement:
constructing a neural radiance field for a three-dimensional scene based on a multi-view image sequence in the three-dimensional scene (Paragraph 0084-0085 – “the virtual walkthrough video generation system 10 can obtain a neural radiance field model 12 associated with an environment. The neural radiance field model 12 may have been trained on a plurality of images of the environment…The neural radiance field model 12 can process a plurality of positions 14 associated with a path to generate a plurality of view synthesis renderings 16 of the environment. The plurality of positions 14 can be associated with a plurality of locations within the environment. The path can be associated with a route through the environment. The path can be manually determined, can be based on image-capture positions, and/or may be automatically determined based on regions of interest in the environment, locations of entrance and exit, and/or based on determined pathways”; Note: a neural radiance field model is generated based on a plurality of image-captured positions of a path, which is equivalent to the multi-view image sequence. The environment is equivalent to the 3D scene);
for each given point location center, determining ray sampling points at the point location center and color information of the ray sampling points based on the neural radiance field (Paragraph 0125, 0152, 0303 – “The NeRF prediction system (a) depicted in FIG. 6A can sample points x along rays that are traced from the camera center of projection 602 through each pixel, then encodes those points with a positional encoding (PE) γ to produce a feature γ(x)… A two-dimensional view direction and a three-dimensional position can be processed with the neural radiance field model 904 to generate prediction data 906. The prediction data 906 can include one or more predicted density values and/or one or more predicted color values…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: ray sampling points and color information of those points are determined. The camera center of projection is equivalent to the given point location center);
performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center (Paragraph 0121-0122, 0303 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour. The rendering can include rendering a three-dimensional model 564. The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data…the neural radiance field model can be utilized to learn depth values and color values for an environment. The learned color representations 572 and the learned depth representations 574 can be utilized to generate view synthesis renderings 570 of the environment…To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: the color information of the sampled points of the ray are rendered into multi-sphere image 562. The rendering is multi-layered since multiple spheres are rendered around a position).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Montero in view of Muhlhausen et al. (Immersive Free-Viewpoint Panorama Rendering from Omnidirectional Stereo Video), hereinafter Muhlhausen.
Regarding claim 2, Montero teaches the method of claim 1. Montero does not teach wherein the performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center, comprises: performing depth rendering on ray sampling points of each radiation ray at the point location center, to obtain a minimum radiation depth and maximum radiation depth at the point location center; determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and a preset depth relationship between adjacent layers; based on the depth information of each layer and the depth information of the ray sampling points, performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center. However, Muhlhausen teaches performing depth rendering on ray sampling points of each radiation ray at the point location center, to obtain a minimum radiation depth and maximum radiation depth at the point location center (Fig. 3C, Paragraph 1 in 2nd Col. of Page 4 – “this representation is able to acquire colour and volume density values for any 3D position, as each spherical layer can have an arbitrary radius r. Generally, for rendering views from an MSI representation, all camera rays are traced through all N layers {Lri = (cri, σri) | i = 1,...,N} to compute the resulting colour”; Note: depth rendering occurs at point center c. The rays are traced through all N layers, where the first layer radius is the minimum depth and the Nth layer radius is the maximum depth; see screenshot of Fig. 3C below); determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and a preset depth relationship between adjacent layers (Paragraph 3 in 1st Col. of Page 5, Paragraph 5 in 2nd Col. of Page 5 – “the user first needs to define the number and radii for the layers of the MSI or use the same layout from training. While the number of layers needs to be balanced between depth precision (higher) and computational load (lower), the radii of the layers only influence depth quality. Besides manual placement, a sufficient automatic approach is to equally distribute the layers within the disparity range of the scene”; Note: the maximum depth and minimum depth is represented by the disparity range. Equal distribution is a type of preset depth relationship between the layers); based on the depth information of each layer and the depth information of the ray sampling points, performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center (Fig. 3C, Paragraph 1 in 2nd Col. of Page 4 – “Each layer consists of RGB colour cr :(θ,φ)T → R3 and volume density values σr :(θ,φ)T → R. Hereby, the volume density σr(θ,φ) corresponds to the prob ability of a camera ray terminating at the infinitesimal thin layer Lr(θ,φ). Similar to planar neural radiance fields, this representation is able to acquire colour and volume density values for any 3D position, as each spherical layer can have an arbitrary radius r. Generally, for rendering views from an MSI representation, all camera rays are traced through all N layers {Lri = (cri, σri) | i = 1,...,N} to compute the resulting colour…
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resembling the probability of radiance from layer Lri to reach the camera without hitting any object. δdi denotes the distance the ray travelled between layer Lri−1 and Lri”; Note: based on the radii/depth of each layer and the 3D position/depth of each point being computed, a multi-sphere image is rendered at a point center c).
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Screenshot of Fig. 3C (taken from Muhlhausen)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to perform depth rendering and obtain the minimum and maximum depth because knowing the depth range allows for setting bounds on the 3D scene and preventing distortion when there is motion in viewing the scene. It also would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to determine the number of layers and the depth information of each layer because the arrangement and number of layers is important for rendering quality and overhead: “While the number of layers needs to be balanced between depth precision (higher) and computational load (lower), the radii of the layers only influence depth quality” (Muhlhausen: Paragraph 3 in 1st Col. of Page 5). Finally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to perform multi-layer rendering on the color information of the ray sampling points based on the depth information because “As an evolution of MPIs for VR applications, MSIs are also feasible for real-time rendering and further support full head rotations… for rendering views from an MSI representation, all camera rays are traced through all N layers” (Muhlhausen: Paragraph 1 in 2nd Col. of Page 4). In other words, rendering an MSI requires rendering by the layer, and the layers are beneficial for supporting head movement in VR. Additionally, the depth information assists in determining the 3D positions of the ray sampling points for rendering.
Regarding claim 4, Montero in view of Muhlhausen teaches the method of claim 2. Montero does not teach wherein based on the depth information of each layer and the depth information of the ray sampling points, performing multi-layer rendering on the color information of the ray sampling points to obtain a multi-sphere image reconstructed for the three-dimensional scene at the point location center, comprises: based on the depth information of each layer and the depth information of ray sampling points, determining associated ray sampling points of each layer; performing volume rendering on the color information of the associated ray sampling points of each layer, to obtain the multi-sphere image reconstructed for the three-dimensional scene at the point location center. However, Muhlhausen teaches based on the depth information of each layer and the depth information of ray sampling points, determining associated ray sampling points of each layer (Fig. 3C, Paragraph 1 in 2nd Col. of Page 4 – “Each layer consists of RGB colour cr :(θ,φ)T → R3 and volume density values σr :(θ,φ)T → R. Hereby, the volume density σr(θ,φ) corresponds to the prob ability of a camera ray terminating at the infinitesimal thin layer Lr(θ,φ). Similar to planar neural radiance fields, this representation is able to acquire colour and volume density values for any 3D position, as each spherical layer can have an arbitrary radius r. Generally, for rendering views from an MSI representation, all camera rays are traced through all N layers {Lri = (cri, σri) | i = 1,...,N} to compute the resulting colour…
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resembling the probability of radiance from layer Lri to reach the camera without hitting any object. δdi denotes the distance the ray travelled between layer Lri−1 and Lri”; Note: based on the radii/depth of each layer and the 3D position/depth of each point being computed, point values of the casted rays are determined at each layer); performing volume rendering on the color information of the associated ray sampling points of each layer, to obtain the multi-sphere image reconstructed for the three-dimensional scene at the point location center (Fig. 3C, Paragraph 1 in 2nd Col. of Page 4 – “Each layer consists of RGB colour cr :(θ,φ)T → R3 and volume density values σr :(θ,φ)T → R. Hereby, the volume density σr(θ,φ) corresponds to the prob ability of a camera ray terminating at the infinitesimal thin layer Lr(θ,φ). Similar to planar neural radiance fields, this representation is able to acquire colour and volume density values for any 3D position, as each spherical layer can have an arbitrary radius r. Generally, for rendering views from an MSI representation, all camera rays are traced through all N layers {Lri = (cri, σri) | i = 1,...,N} to compute the resulting colour…
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resembling the probability of radiance from layer Lri to reach the camera without hitting any object. δdi denotes the distance the ray travelled between layer Lri−1 and Lri”; Note: a multi-sphere image is rendered at a center point c based on the colors of points from rays that are casted through each layer). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to use depth information to determine the associated rays of each layer because the depth information (radius) helps determine the 3D position and the values at the 3D position can then be determined for rendering the layer. Since Montero already teaches performing volume rendering on color information of ray sampling points (Paragraph 0303 – “NeRF's multilayer perceptron (MLP) network can obtain a three-dimensional position and two-dimensional viewing direction as input and can output volume density and color. To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”), it also would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to perform volume rendering on the color information of each layer because “As an evolution of MPIs for VR applications, MSIs are also feasible for real-time rendering and further support full head rotations… for rendering views from an MSI representation, all camera rays are traced through all N layers” (Muhlhausen: Paragraph 1 in 2nd Col. of Page 4). In other words, rendering an MSI requires rendering by the layer, and the layers are beneficial for supporting head movement in VR.
Claims 3 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Montero in view of Muhlhausen and Broxton et al. (Immersive Light Field Video with a Layered Mesh Representation), hereinafter Broxton.
Regarding claim 3, Montero in view of Muhlhausen teaches the method of claim 2. Montero does not teach wherein the determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and a preset depth relationship between adjacent layers, comprises: taking the minimum radiation depth as the depth information of a first layer and take the first layer as the current layer; performing a multi-layer depth determination step: determining the depth information of a next layer based on the depth information of the current layer and a preset depth relationship between adjacent layers; taking the next layer as the new current layer, and continuing to perform the multi-layer depth determination step until the depth information of the latest layer is greater than or equal to the maximum radiation depth, so as to obtain the number of layers and the depth information of each layer at the point location center. However, Broxton teaches taking the minimum radiation depth as the depth information of a first layer and take the first layer as the current layer (Paragraph 1 in 1st Col. of Page 15 – “From Equation 6 we see that MSI layer spacing depends on only two variables: (1) the radius 𝑟𝑖 of the interpolation volume, and (2) the angular sampling rate 𝜌 of the MSI layer textures. Starting at “near” shell radius 𝑟0 and iterate using Equation 6 until a “far” shell radius of 𝑟max = ∞ is reached, we can compute the total number of shells required to satisfy the ILD condition”; Note: the “near” shell radius, which is equivalent to the minimum radiation depth, is taken as the first and current layer when iteration begins); performing a multi-layer depth determination step: determining the depth information of a next layer based on the depth information of the current layer and a preset depth relationship between adjacent layers (Paragraph 1 in 1st Col. of Page 15 – “
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From Equation 6 we see that MSI layer spacing depends on only two variables: (1) the radius 𝑟𝑖 of the interpolation volume, and (2) the angular sampling rate 𝜌 of the MSI layer textures. Starting at “near” shell radius 𝑟0 and iterate using Equation 6 until a “far” shell radius of 𝑟max = ∞ is reached, we can compute the total number of shells required to satisfy the ILD condition”; Note: rn+1, which is the depth information of a next layer, is determined based on the current depth layer rn and the depth relationship shown in equation 6 above); taking the next layer as the new current layer, and continuing to perform the multi-layer depth determination step until the depth information of the latest layer is greater than or equal to the maximum radiation depth, so as to obtain the number of layers and the depth information of each layer at the point location center (Paragraph 1 in 1st Col. of Page 15 – “
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From Equation 6 we see that MSI layer spacing depends on only two variables: (1) the radius 𝑟𝑖 of the interpolation volume, and (2) the angular sampling rate 𝜌 of the MSI layer textures. Starting at “near” shell radius 𝑟0 and iterate using Equation 6 until a “far” shell radius of 𝑟max = ∞ is reached, we can compute the total number of shells required to satisfy the ILD condition”; Note: the process continues to iterate until the maximum depth 𝑟max is reached. The number of shells/layers is obtained and the depth information is provided by the output of equation 6 in each iteration). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Broxton to iteratively determine the depth information at each layer starting from the minimum to the maximum depth layer because “In order to render views without aliasing, the ILD condition must hold for any ray that passes through the interpolation volume whose radius is 𝑟𝑖…we can compute the total number of shells required to satisfy the ILD condition” (Broxton: Paragraph 2 in 2nd Col. of Page 14, Paragraph 1 in 1st Col. of Page 15). In other words, knowing the depth information for each layer is important for rendering and ensuring that the conditions are satisfied for proper rendering.
Regarding claim 5, Montero in view of Muhlhausen teaches the method of claim 2. Montero does not teach wherein the depth relationship between adjacent layers satisfies that in the point location, the maximum pixel parallax of an observable line of sight for any pixel point of the previous layer in the adjacent layer, when falling on the subsequent layer in the adjacent layer, is less than or equal to the unit pixel. However, Broxton teaches wherein the depth relationship between adjacent layers satisfies that in the point location, the maximum pixel parallax of an observable line of sight for any pixel point of the previous layer in the adjacent layer, when falling on the subsequent layer in the adjacent layer, is less than or equal to the unit pixel (Paragraph 1 in 2nd Col. of Page 14 – “maximum disparity seen by the viewer between any two adjacent layers must be ≤ 1 pixel”: Note: the maximum disparity seen by a viewer is equivalent to the maximum pixel parallax of an observable line of sight. 1 pixel is equivalent to the unit pixel). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Broxton to have the maximum pixel parallax be less than or equal to the unit pixel “In order to render views that do not alias or ‘tear apart’ as the viewer moves from side to side in the interpolation volume” (Broxton: Paragraph 1 in 1st Col. of Page 14). In other words, limiting the pixel parallax helps to prevent distortion and artifacts.
Regarding claim 6, Montero in view of Muhlhausen and Broxton teaches the method of claim 5. Montero does not teach wherein an initial radius of the point location is a preset expected roaming depth, and if the number of layers at the point location center is greater than a preset upper limit of layers, the method further comprises: re-determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and the preset depth relationship between adjacent layers at the point location center by reducing the radius of the point location in the depth relationship between adjacent layers, so that the re-determined number of layers is less than or equal to the preset upper limit of layers. However, Muhlhausen teaches wherein an initial radius of the point location is a preset expected roaming depth (Paragraph 4 in 1st Col. of Page 4, Paragraph 3 in 1st Col. of Page 5 – “Capturing the scene is only the first step towards a full 6-DOF light field video reconstruction. In this section we introduce our multi sphere image representation and show how to construct it to be compatible with our capture geometry…Using the left and right view of an input ODS video frame along with a set of radii R={ri | i = 1,...,N}, our network produces an MSI with N spherical layers LR ={Lr | r ∈ R} that enable the user to experience head-motion parallax in VR environments”; Note: the radius of the MSI represents a preset expected roaming depth since it shows a scene for a VR environment that has 6-DOF). Since Montero already teaches a sphere representing an expected roaming area (Paragraph 0121 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed. The sphere 562 can move with the movement of the virtual tour”), it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Muhlhausen to have an initial radius be a preset expected roaming depth because all spheres have a radius, and if the sphere is a roaming area, then logically, the radius represents a measure of the depth of the roaming area. Montero modified by Muhlhausen still does not teach if the number of layers at the point location center is greater than a preset upper limit of layers, the method further comprises: re-determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and the preset depth relationship between adjacent layers at the point location center by reducing the radius of the point location in the depth relationship between adjacent layers, so that the re-determined number of layers is less than or equal to the preset upper limit of layers. However, Broxton teaches if the number of layers at the point location center is greater than a preset upper limit of layers, the method further comprises: re-determining the number of layers and the depth information of each layer at the point location center based on the minimum radiation depth, the maximum radiation depth and the preset depth relationship between adjacent layers at the point location center by reducing the radius of the point location in the depth relationship between adjacent layers (Fig. 5A and 5B, Paragraph 3 in 1st Col. of Page 6, Paragraph 1-2 in 2nd Col. of Page 6 – “We subdivide the MSI layers into discrete depth ranges which we call Layer Groups, each with an equal number of consecutive layers…We chose to segment MSIs into 16 LM layers, as this was the highest attainable quality within our performance budget — more layers increased rendering overhead and decreased atlasing efficiency… we collapse the MSI layers within each Layer Group into a single depth map using standard alpha compositing (Equation 1). How ever, instead of compositing RGB values we instead use the index of the layer, i.e. the layer disparity. We perform this over blend from the central viewpoint of the interpolation volume”; Note: 16 layers is set as the upper limit since it is the highest number within the performance budget. The layers are grouped/re-determined by increasing the depth interval, which reduces the radius. It is obvious that the grouping is based on the minimum and maximum depth and a preset depth relationship because each layer group has an equal number of layers in it and a discrete depth range, which means the layers are evenly divided; see screenshot of Fig. 5A and 5B below), so that the re-determined number of layers is less than or equal to the preset upper limit of layers (Paragraph 1 in 2nd Col. of Page 6 – “We chose to segment MSIs into 16 LM layers, as this was the highest attainable quality within our performance budget — more layers increased rendering overhead and decreased atlasing efficiency”; Note: after grouping the layers, there are 16 layers, which is equal to the upper limit since it is the highest possible number within the performance budget).
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Screenshot of Fig. 5A and 5B (taken from Broxton)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to incorporate the teachings of Broxton to re-determine the number of layers and the depth information of each layer so that the number of layers is less than or equal to a preset upper limit for the benefit of achieving “the highest attainable quality within our performance budget — [adding] more layers increased rendering overhead and decreased atlasing efficiency” (Broxton: Paragraph 1 in 2nd Col. of Page 15).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Montero.
Regarding claim 7, Montero teaches the method of claim 1. Montero further teaches according to the neural radiance field, determining a multi-view image sample sequence in the point location in a plurality of preset sampling poses (Paragraph 0104, 0112 – “training the one or more neural radiance field models based on the plurality of images can include determining a plurality of respective scene positions and a plurality of respective scene view directions for the plurality of images based on comparing feature location and feature sizes between images…Obtaining images of the environment for training the neural radiance field model can be based on a set framework of rules and procedures. For example, the plurality of images can be captured by performing a set path with set image-capture directions”; Note: the plurality of images of the scene is equivalent to the multi-view image sample sequence, and the set image-capture directions is equivalent to the preset sampling poses), performing volume rendering on color information of intersection points between a projected light ray in each sampling pose and the multi-sphere image reconstructed at the point location center (Paragraph 0121, 0125, 0131, 0303 – “The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed…The NeRF prediction system (a) depicted in FIG. 6A can sample points x along rays that are traced from the camera center of projection 602 through each pixel… the three-dimensional rays of Fig. 6C may represent sampling directions…NeRF’s multilayer perceptron (MLP) network can obtain a three-dimensional position and two-dimensional viewing direction as input and can output volume density and color. To render each pixel in an output image, NeRF can use volume rendering to combine the colors and densities from many points sampled along the corresponding three-dimensional ray”; Note: volume rendering occurs on the colors of points along a ray. Each ray extends between each viewing direction (sampling pose) and pixels of the image, which in this case, is a multi-sphere image), so as to obtain a multi-view rendered image sequence in the point location based on the difference between the multi-view image sample sequence and the multi-view rendered image sequence (Paragraph 0104, 0114 – “Training the one or more neural radiance field models can include evaluating a loss function that evaluates a difference between the one or more predicted view synthesis renderings and one or more respective images of the plurality of images and adjusting one or more parameters of the one or more neural radiance field model based at least in part on the loss function… Once the one or more neural radiance field models are trained, a path 430 can be determined. A plurality of positions within the path 430 can be processed with the one or more neural radiance field models to generate a plurality of view synthesis renderings of the environment”; Note: a difference between rendered images and sample/training images is calculated. After training, a plurality of view synthesis renderings, which is equivalent to a multi-view rendered image sequence, is obtained), optimizing the multi-sphere image reconstructed at the point location center (Paragraph 0104, 0121, 0296, 0303 – “adjusting one or more parameters of the one or more neural radiance field model based at least in part on the loss function…The rendering system can include real-time rendering that may include neural radiance field rendering within one or more spheres 562 around a position associated with a position being viewed…A NeRF system may directly optimize a neural volumetric scene representation to match all input images using gradient descent on a rendering loss…A neural radiance field (NeRF) model can include a neural network based scene representation that is optimized to reproduce the appearance of a set of input images with known camera pose”; Note: the rendering is optimized by adjusting parameters. The rendering, in this case, is the rendering of multiple spheres at a position (point location center)).
Regarding claim 14, Montero teaches the method of claim 13. Montero separately teaches wherein, the roaming position information is located in the boundary area of the first type of roaming point location (Fig. 5F, Paragraph 0124 – “The rendering sphere system 590 can include neural radiance field model rendering within a first distance 592 and a second distance 594 around the user”; Note: the roaming position information is in the boundary area 594. See screenshot of Fig. 5F above), and the roaming position information is spatially compressed in a nonlinear compression mode (Paragraph 0121, 0130 – “The three-dimensional model can be rendered for perspective such that the neural radiance field renderings within the sphere 562 may be warped based on predicted depth data …the neural radiance field models disclosed herein may leverage non-linear scene parametrization, online distillation, and/or a distortion-based regularizer for unbounded scenes and/or to generate three-hundred and sixty degree scene renderings. The neural radiance field models may leverage a space-warping procedure to shrink distant points towards the origin”; Note: the non-linear scene parametrization and space-warping is equivalent to nonlinear spatial compression). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Montero to combine the features to spatially compress the roaming position information in a nonlinear compression mode in response to the roaming position information being located in the boundary area of the first type of roaming point location for the benefit of bounding the user into the boundary area, reducing distortion, and making the virtual world more realistic based on the warping.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Malin et al. (US 20210327119 A1) teaches a method of reconstructing a 3D scene by representing a plurality of images of a physical environment as a sphere with projected 3D points and associated depths. Attal et al. (MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images) teaches a method of transforming stereo 360 degree images into multi-sphere images in order to estimate depth and visibility. Li et al. (Extending 6-DoF VR Experience Via Multi-Sphere Images Interpolation) teaches a method of interpolating multi-sphere images to generate intermediate multi-sphere images and enhance VR views and movement.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE HAU MA whose telephone number is (571)272-2187. The examiner can normally be reached M-Th 7-5:30.
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/MICHELLE HAU MA/ Examiner, Art Unit 2617
/KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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