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 .
Allowable Subject Matter
Claim 4-8, 11-13 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 states, “3. The image processing apparatus according to claim 2, wherein in a case where any of a plurality of viewpoints represented by the obtained plurality of pieces of viewpoint information does not satisfy a condition, viewpoint information representing a specific viewpoint is associated with each of the plurality of groups by taking a virtual viewpoint different from the plurality of viewpoints represented by the plurality of pieces of viewpoint information as the specific viewpoint.” It is largely difficult to parse what is intended from the claim. The claim is asserting a case that doesn’t satisfy condition but it is not clear exactly what happens either. It is not clear what is claimed because it is not clear how something can not satisfy a condition without the condition having an option satisfy the condition. As this isn’t expressed the claim is indefinite. Claim 3 is so unclear that prior art can’t be applied.
Claim 4 and the dependent claims are not considered to be indefinite sense it allows for an option to satisfy the condition.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Roimela et al. (US 20240282051 A1) in view of Li et al. (US 20190251734 A1).
Regarding claim 1, Roimela teaches an image processing apparatus comprising (¶26, “In a third aspect, an embodiment of the present disclosure provides a computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when executed by a processor, cause the processor to execute steps of a computer-implemented method of the first aspect.” The processor is carrying out image processing.):
one or more memories storing instructions (¶26, “In a third aspect, an embodiment of the present disclosure provides a computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when executed by a processor, cause the processor to execute steps of a computer-implemented method of the first aspect.”); and
one or more processors executing the instructions to perform (¶26, “In a third aspect, an embodiment of the present disclosure provides a computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when executed by a processor, cause the processor to execute steps of a computer-implemented method of the first aspect.”):
obtaining three-dimensional shape data of an object captured in a plurality of captured images whose viewpoints are different (See abstract, “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point.” 3d position of point or 3d structure(s) are considered to be shape data. ¶67, “ In some implementations, both the given colour image and the given depth image are captured using a single camera. As an example, the aforesaid images may be captured as an RGB-D image using the single camera. In other implementations, the given colour image and the given depth image are captured separately by using separate cameras.” Two separate cameras capture from different viewpoints.);
dividing elements configuring the three-dimensional shape data into a plurality of groups (See abstract, “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point.””);
associating each of the plurality of groups with viewpoint information representing a specific viewpoint (See abstract “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point.”);
generating a two-dimensional map based on the plurality of groups and the viewpoint information representing the specific viewpoint, which is associated with each group (See abstract, “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point” The examiner notes each divided space or color image that is divided into color tiles/depth tiles presents a specific view point which is associated with the divided group however it is divided The two dimensional map is considered to be the tile divisions.); and
wherein
the specific viewpoint includes a virtual viewpoint different from the viewpoint of each of the plurality of captured images (See ¶181, describes new color tiles from image reconstruction. ¶195, describes new depth tiles from image reconstruction. ¶3, describes new viewpoints happen from image reconstruction. New viewpoints are view points that are different and didn’t exist before and therefore are not the captured view point(s)) but doesn’t explicitly disclose generating a texture image representing a color of the object based on the two-dimensional map.
Li teaches generating a texture image representing a color of the object based on the two-dimensional map (¶21, “Free viewpoint video” (FVV) refers generally to video data in which the frame data is provided in a format that allows a viewpoint to be selected or modified as the frames are being rendered and presented on a display. Frame data for FVV (or other 31) video data) may include data that provides a geometric description of one or more 3D objects to be rendered, referred to herein as “object geometry.” Object geometry can be specified using one or more meshes, i.e. sets of interconnected vertices, that represent the surface as a set of connected polygons (e.g., triangles). Each vertex in a mesh has specified coordinates (usually in an object-relative coordinate space) and connectivity to adjacent vertices (which may be expressly specified or implicit, e.g., in the ordering of vertices in a data structure), and a mesh can include any number of vertices. Each vertex may have other associated attributes, such as a color and/or coordinates in a texture space that defines a texture to be applied to the mesh (or a portion thereof); texture spaces are typically defined using two-dimensional coordinates (referred to as uv coordinates), although other systems may be used. Depending on implementation, a vertex may have one or more associated textures, and one or more textures may be applied to an object's surface or portions thereof.” Also see ¶23).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Roimela in view of Li as it adds intricate surface detail, realism, and color to 3D models without increasing polygon count, thus increasing realism.
Regarding claim 2, Roimela in view of Li teaches the image processing apparatus according to claim 1, wherein along with the three-dimensional shape data (See abstract, “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point.” See Fig. 2C, ¶283, “With reference to FIG. 2C, the 3D space represents the living room in the real-world environment. The 3D space is shown to be divided into the 3D grid of 64 equi-sized voxels (depicted as a 4×4×4 3D grid of dash-dot lines). For sake of simplicity, the 3D space is divided into only 64 voxels, and one of the 64 voxels that is located at an upper right corner of the 3D space is shown in a complete 3D form.” ¶283 further explains Fig. 2C.), a plurality of pieces of viewpoint information representing the viewpoint of each of the plurality of captured images is obtained (¶17, “receive a plurality of colour images of a given real-world environment, a plurality of depth images corresponding to the plurality of colour images, and viewpoint information indicative of corresponding viewpoints from which the plurality of colour images and the plurality of depth images are captured, wherein three-dimensional (3D) positions and orientations of the viewpoints are represented in a given coordinate system; ” Viewpoint information, 3d position and orientation all are considered to be viewpoint information.) and
based on the plurality of pieces of viewpoint information, the elements configuring the three-dimensional shape data are divided into a plurality of groups (See ¶17 as cited above. ¶18, “divide a 3D space occupied by the given real-world environment into at least one 3D grid of voxels, wherein the at least one 3D grid is represented in the given coordinate system;”).
Regarding claim 14, Roimela in view of Li teaches the image processing apparatus according to claim 1, wherein
the two-dimensional map is a two-dimensional map corresponding to the three-dimensional shape data, in which two-dimensional pieces obtained by performing UV development for each group are arranged and
the texture image is generated by calculating the pixel value of each pixel included in each two-dimensional piece on the two-dimensional map by using the plurality of captured images (See ¶21, ““Free viewpoint video” (FVV) refers generally to video data in which the frame data is provided in a format that allows a viewpoint to be selected or modified as the frames are being rendered and presented on a display. Frame data for FVV (or other 31) video data) may include data that provides a geometric description of one or more 3D objects to be rendered, referred to herein as “object geometry.” Object geometry can be specified using one or more meshes, i.e. sets of interconnected vertices, that represent the surface as a set of connected polygons (e.g., triangles). Each vertex in a mesh has specified coordinates (usually in an object-relative coordinate space) and connectivity to adjacent vertices (which may be expressly specified or implicit, e.g., in the ordering of vertices in a data structure), and a mesh can include any number of vertices. Each vertex may have other associated attributes, such as a color and/or coordinates in a texture space that defines a texture to be applied to the mesh (or a portion thereof); texture spaces are typically defined using two-dimensional coordinates (referred to as uv coordinates), although other systems may be used. Depending on implementation, a vertex may have one or more associated textures, and one or more textures may be applied to an object's surface or portions thereof.”, ¶39, “ At block 206, a consistent texture atlas for each frame of a segment is generated, and texture coordinates are re-projected so that the vertices of the source mesh of the CMS are mapped to the consistent texture atlas. As used herein, a “consistent texture atlas,” or “CTA,” defines a single texture-coordinate space that can include all of the textures used in a frame. FIG. 5 shows a flow diagram of a CTA generation process 500 that can be implemented; e.g., at block 206. At block 502, the texture(s) associated with the mesh at a given frame is (are) added to the CTA, for instance by adding the texels of the texture to a currently unused region of the CTA coordinate space. At block 504, coordinates in the CTA coordinate space corresponding to the original uv coordinates of each vertex in the source mesh are determined, and at block 506, the vertex data for the source mesh of the CMS is modified to include the CTA coordinates. At block 508, a CTA can be generated for one or more other frames of the segment, with the mapping between vertices of the mesh and coordinates in the CTA coordinate space being consistent across frames. Process 500 can be repeated for each CMS in a given segment. In some embodiments, the result of process 500 is a CTA for each frame of the segment and a mapping of the vertices of the mesh to coordinates in the CTA.”).
Regarding claim 15, Roimela in view of Li teaches the image processing apparatus according to claim 14, wherein
the texture image is generated by calculating the pixel value of each pixel whose pixel center is included within the two-dimensional piece by using a captured image in which a group corresponding to the two-dimensional piece is captured among the plurality of captured images (See Li ¶23, ¶39).
Regarding claim 16, Roimela in view of Li teaches the image processing apparatus according to claim 1, wherein
the element configuring the three-dimensional shape data is a polygon and
the three-dimensional shape data is mesh data representing a surface shape of the object by a set of the polygons (See ¶21, ““Free viewpoint video” (FVV) refers generally to video data in which the frame data is provided in a format that allows a viewpoint to be selected or modified as the frames are being rendered and presented on a display. Frame data for FVV (or other 31) video data) may include data that provides a geometric description of one or more 3D objects to be rendered, referred to herein as “object geometry.” Object geometry can be specified using one or more meshes, i.e. sets of interconnected vertices, that represent the surface as a set of connected polygons (e.g., triangles). Each vertex in a mesh has specified coordinates (usually in an object-relative coordinate space) and connectivity to adjacent vertices (which may be expressly specified or implicit, e.g., in the ordering of vertices in a data structure), and a mesh can include any number of vertices. Each vertex may have other associated attributes, such as a color and/or coordinates in a texture space that defines a texture to be applied to the mesh (or a portion thereof); texture spaces are typically defined using two-dimensional coordinates (referred to as uv coordinates), although other systems may be used. Depending on implementation, a vertex may have one or more associated textures, and one or more textures may be applied to an object's surface or portions thereof., ¶23, ¶39).
Regarding claim 17, Roimela in view of Li teaches the image processing apparatus according to claim 1, wherein
the viewpoint information at least includes information identifying the position and orientation of a corresponding viewpoint (See Fig. 2C, ¶283, “With reference to FIG. 2C, the 3D space represents the living room in the real-world environment. The 3D space is shown to be divided into the 3D grid of 64 equi-sized voxels (depicted as a 4×4×4 3D grid of dash-dot lines). For sake of simplicity, the 3D space is divided into only 64 voxels, and one of the 64 voxels that is located at an upper right corner of the 3D space is shown in a complete 3D form.” ¶283 further explains Fig. 2C.), a plurality of pieces of viewpoint information representing the viewpoint of each of the plurality of captured images is obtained (¶17, “receive a plurality of colour images of a given real-world environment, a plurality of depth images corresponding to the plurality of colour images, and viewpoint information indicative of corresponding viewpoints from which the plurality of colour images and the plurality of depth images are captured, wherein three-dimensional (3D) positions and orientations of the viewpoints are represented in a given coordinate system; ” Viewpoint information, 3d position and orientation all are considered to be viewpoint information.).
Regarding claim 18, Roimela teaches an image processing method comprising the steps of (See abstract, “A system and method for receiving colour images, depth images and viewpoint information; dividing 3D space occupied by real-world environment into 3D grid(s) of voxels; create 3D data structure(s) comprising nodes, each node representing corresponding voxel; dividing colour image and depth image into colour tiles and depth tiles, respectively; mapping colour tile to voxel(s) whose colour information is captured in colour tile; storing, in node representing voxel(s), viewpoint information indicative of viewpoint from which colour and depth images are captured, along with any of: colour tile that captures colour information of voxel(s) and corresponding depth tile that captured depth information, or reference information indicative of unique identification of colour tile and corresponding depth tile; and utilising 3D data structure(s) for training neural network(s), wherein input of neural network(s) comprises 3D position of point and output of neural network(s) comprises colour and opacity of point.”). The rest of the limitations of claim 18 recites similar limitations to that of claim 1 and thus is rejected under similar rationale as detailed above.
Regarding claim 19 teaches Roimela teaches a non-transitory computer readable storage medium storing a program for causing a computer to perform an image processing method comprising the steps of (¶26, “In a third aspect, an embodiment of the present disclosure provides a computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when executed by a processor, cause the processor to execute steps of a computer-implemented method of the first aspect” and abstract):
The rest of the limitations of claim 19 recites similar limitations to that of claim 1 and thus is rejected under similar rationale as detailed above.
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Roimela et al. (US 20240282051 A1) in view of Li et al. (US 20190251734 A1) in further view Kim et al. (View-dependent Transmission of Three-dimensional Mesh Models Using Hierarchical Partitioning).
Regarding claim 9, Roimela in view of Li teaches the image processing apparatus according to claim 1, wherein but doesn’t explicitly disclose
the division is performed based on a normal vector of the element configuring the three-dimensional shape data.
Kim teaches the division is performed based on a normal vector of the element configuring the three-dimensional shape data (See Fig. 1, page 1929, second paragraph, “The input data for hierarchical partitioning is geometry and connectivity information of the 3-D mesh model. We select the initial vertices as many as the number of the initial submeshes. For mesh partitioning [6], we can apply a multi-seed traversal technique [3] that is one of the well-known partitioning techniques. We perform hierarchical partitioning recursively until we have the specified number of levels. In other words, one parent submesh is divided into child submeshes as many as the number of the initial submeshes in the current level. Then, if the current level is less than the
specified one, all child submeshes become parent submeshes in the next level and each submesh is partitioned again. Consequently, we construct the parent-child relationship in a tree structure. Fig. 1 shows an example of hierarchical partitioning when the number of the initial submeshes is two and the specified number of levels is three.” See page 1931 last paragraph that is incomplete – page 1932 paragraph 3, the paragraph right after figure 4. Also see figure 4.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Roimela in view of Li in further view of Kim as it would have been obvious to use the known technique Kim with Roimela in view of Li to use a known technique to improve similar apparatus as outlined in Kim in the same way.
Regarding claim 10, Roimela in view of Li in further view of Kim teaches the image processing apparatus according to claim 9, wherein
viewpoint information representing a specific viewpoint is associated with each of the plurality of groups by taking a virtual viewpoint having a line-of-sight direction along a normal vector of an element configuring the group as the specific viewpoint (See previous citations and expiation of claim 9 as detailed above. Also see figure 4, “There are two situations when we decide the resolution of submeshes: static viewing and dynamic viewing. In static viewing, we can determine resolutions with the initial viewing parameter. On the other hand, we re-estimate resolutions with the changed viewing parameter in dynamic viewing. Fig. 4 illustrates the resolution decision.”).
Conclusion
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/ROBERT J CRADDOCK/Primary Examiner, Art Unit 2618
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