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 .
Response to Amendment
The Amendment filed February 4th 2026 has been entered. Claims 205-207, 209-217 and 223-237 are pending in the application. Claim 237 is newly added. Further search was performed to address the amended limitations and newly added claims. Newly found references Golparavar-Fard (US 20190325089 A1) and Narayanan (NPL: Virtual Worlds using Computer Vision) were used for the amended claim limitations.
Response to Arguments
Applicant’s arguments with respect to claim(s) 205-207, 209-217, and 223-237 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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 205, 209, 210, 212, 213, 214, 215, 223, 224, 227, 228, 230, 231, 232, 233, 236, and 237 are rejected under 35 U.S.C. 103 over Golparavar-Fard (US 20190325089 A1) in view of Narayanan (NPL: Virtual Worlds using Computer Vision).
Regarding claim 205:
Golparavar-Fard teaches:
A method for generating a three-dimensional (3D) representation, the method comprising:
receiving a plurality of calculated real camera poses associated with a plurality of images (Golparavar-Fard: The computer storage may store a 3D BIM of a construction site, anchor images, each depicting a viewpoint having a 3D pose and containing features with known 3D positions [0045]);
generating a point cloud based on the plurality of images, wherein the point cloud comprises a plurality of points (Golparavar-Fard: executes a reconstruction algorithm using the image and the anchor images to calibrate the image to the BIM and generate an initial 3D point cloud model. [0083]);
selecting calculated real camera poses based on an association with a target camera image (Golparavar-Fard: the subset of anchor images may be identified by the date at which the anchor images were photographed (e.g., using the most recent photos), or by matching features in the target images to the calibrated, anchor images. [0074]; see Note 205A), wherein the selected calculated real camera poses real cameras comprise a subset of the plurality of calculated real camera poses real cameras; and
generating a 3D representation comprising a subset of the plurality of points of the point cloud (Golparavar-Fard: The processing device may further display a visual instantiation of the updated 3D point cloud model of the construction site in a graphical user interface of a display device, wherein the visual instantiation of the updated 3D point cloud model includes 3D points [0047]) from a perspective of the virtual camera pose (Golparavar-Fard: The system 100 may provide a user the ability to view the images together with the point cloud from any camera viewpoint, as illustrated in FIGS. 9A and 9B. [0093]).
Note 205A: In other words, Golparavar-Fard teaches identifying anchor images (which have corresponding 3D poses, as cited above in [0045]) based on matching features in target images.
Golparavar-Fard fails to teach:
selecting calculated real camera poses based on an association with a virtual camera pose
generating a 3D representation comprising a subset of the plurality of points of the point cloud from a perspective of the virtual camera pose based on a relation of the virtual camera pose to the selected calculated real camera poses.
Narayanan teaches:
selecting calculated real camera poses based on an association with a virtual camera pose (Narayanan: Selecting the reference VSM. θi is the angle between the virtual camera’s line of sight and the line joining the target point with the position Vi of VSM i. The VSM with the smallest angle θi is selected as the reference VSM, Pg. 6, Fig.3).
generating a 3D representation comprising a subset of the plurality of points of the point cloud from a perspective of the virtual camera pose (Narayanan: A VLM is a dynamically selected scene model that contains a hole-free description of the scene from the virtual camera’s viewpoint, Pg. 7, Section 4.3: Rendering using a VLM, par. 1) based on a relation of the virtual camera pose to the selected calculated real camera poses (Narayanan: A variation of this approach could have the renderings of the component VSMs cross-dissolved (i.e., weighted averaged) based on, say, a closeness metric or the distances of the virtual camera from the VSM origins., Pg. 7, Section 4.3: Rendering using a VLM, par. 1).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Narayanan with Golparavar-Fard. Selecting calculated real camera poses based on an association with a virtual camera pose, as in Narayanan, would benefit the Golparavar-Fard teachings by enabling the system to fill gaps in the image caused by obscured or unknown areas not shown in the original reference images.
Regarding claim 209:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), further comprising: selecting the virtual camera pose within a spatial constraint, wherein the spatial constraint is established based on the plurality of real camera poses real cameras (Narayanan: The virtual camera could be anywhere, but its position must be specified in terms of epipolar relationships with the input cameras, Pg. 3, Section 2.3.1: Virtual Camera Position, par. 1; Golparavar-Fard: The processing device may further incrementally repeat the last few steps to identify a second image from the subset of the target images and perform [3D reconstruction], […] using the second image and the anchor images as constraints to the image-based reconstruction algorithm [0047]).
Regarding claim 210:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), wherein selecting the real camera poses associated with the virtual camera pose comprises:
comparing each real camera pose of the plurality of real camera poses real cameras to a virtual camera pose (Narayanan: Selecting the reference VSM. θi is the angle between the virtual camera’s line of sight and the line joining the target point with the position Vi of VSM I, Pg. 6, Fig. 3; see Note 210A); and selecting a real camera pose of the plurality of real camera poses real cameras responsive to a distance between a real camera pose and the virtual camera pose being less than a threshold distance value (Narayanan: The VSM with the smallest angle θi is selected as the reference VSM. Pg. 6, Fig. 3).
Note 210A: Narayanan teaches: “A single VSM represents all surfaces within the viewing frustum of the camera visible from the camera location.” (Pg. 5, Section 4.1: The Representation). That is, each “VSM” (Visible surface model) corresponds to a real camera.
Regarding claim 212:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), wherein selecting the real camera poses real cameras associated with the virtual camera pose comprises:
comparing a field of view of each real camera pose of the plurality of real camera poses to a field of view of the virtual camera pose and selecting a real camera pose (Narayanan: The VSM with the closest angle with the virtual camera’s viewing direction is chosen as the reference VSM, Pg. 6, Section 4.2.1, Selecting Reference VSM, col. 2) of the plurality of real camera poses responsive to the field of view of the real camera pose overlapping the field of view of the virtual camera (Narayanan: We use a closeness metric based on the assumptions about the distribution (3D placement) and orientation (field of view) of the VSMs, Pg. 6, Section 4.2.1: Selecting Reference VSM; see Note 212A).
Note 212A: Narayanan teaches: “Our measure of closeness is the angle between this line of sight and the line connecting the target point to the 3D position of the VSM, as shown in Figure 3. […] This [closeness] measure works well when both the virtual viewpoint and all the VSMs are pointed at the same general region of space.” In other words, overlapping fields of view improve the method taught by Narayanan. Therefore, it would be obvious to one of ordinary skill in the art to “compare a field of view of each real camera pose of the plurality of real camera poses to a field of view of the virtual camera pose and select a real camera pose of the plurality of real camera poses responsive to the field of view of the real camera pose overlapping the field of view of the virtual camera.
(Golparavar-Fard: The MVS code 288 may generate a dense point cloud from the sparse point clouds by, from the calibrated images produced by the SfM algorithm, grouping sets of images according to viewpoint and matching patches centered about each pixel in each image to patches in the other images, for example.
Regarding claim 213:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), wherein selecting the real camera poses associated with the virtual camera pose comprises:
comparing capture times associated with the real camera poses of the plurality of real camera poses to one another (see Note 213A); and
selecting real camera poses of the plurality of real camera poses responsive to the real camera poses being temporally proximate to one another (Golparavar-Fard: the subset of anchor images may be identified by the date at which the anchor images were photographed (e.g., using the most recent photos) [0074]).
Note 213A: It would be obvious to one of ordinary skill in the art to compare capture times associated with the real camera poses of the plurality of real camera poses to one another, because determining the subset of anchor images “by the date at which the anchor images were photographed”, for the example given by Golparavar-Fard, would require the dates of each image to be compared with the date of the most recent image.
Regarding claim 214:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), wherein generating the 3D representation comprising the subset of the plurality of points of the point cloud from the perspective of the virtual camera pose comprises:
selecting points of the plurality of points of the point cloud that were observed by the selected real camera poses (Golparavar-Fard: anchor images, each […] containing features with known 3D positions with respect to the 3D BIM [0045]) associated with the virtual camera pose (see Note 214A), wherein the subset of the plurality of points of the point cloud comprises the selected points (Golparavar-Fard: The features may include at least one of structural points, edges, objects, or textured surfaces within the 3D BIM [0045]).
Note 214A: Golparavar-Fard teaches that each “anchor image” has both a 3D pose and “features with known 3D positions with respect to the 3D BIM” [0045]. In the rejection of claim 205, it was shown that Narayanan teaches selecting real camera poses associated with the virtual camera pose: “Selecting the reference VSM. θi is the angle between the virtual camera’s line of sight and the line joining the target point with the position Vi of VSM i. The VSM with the smallest angle θi is selected as the reference VSM,” (Narayanan, Pg. 6, Fig.3). When the teachings of Narayanan are combined with the teachings of Golparavar-Fard, the 3D pose of the real camera would be directly associated with the 3D positions of the features in the image. Therefore, the Examiner understands Golparavar-Fard in view of Narayanan to teach “selecting points of the plurality of points of the point cloud that were observed by the selected real camera poses”.
Furthermore, because the features may be points, as taught by Golparavar-Fard in [0045], and because the features may be represented in the point cloud: “The processing device 110 may further triangulate the subset of matching features between the first image and anchor image that are not already represented in the 3D point cloud.” [0076], it would be obvious for the subset of the plurality of points of the point cloud to include in the selected points.
Regarding claim 215:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), further comprising: generating a two-dimensional (2D) representation of the 3D representation (Golparavar-Fard: The processing device may further display a visual instantiation of the updated 3D point cloud model of the construction site in a graphical user interface of a display device [0047]; Golparavar-Fard: The system 100 may provide a user the ability to view the images together with the point cloud from any camera viewpoint, as illustrated in FIGS. 9A and 9B. [0093]; see Note 215A) from the perspective of the virtual camera pose (Narayanan: A VLM is a dynamically selected scene model that contains a hole-free description of the scene from the virtual camera’s viewpoint, Pg. 7, Section 4.3: Rendering using a VLM, par. 1).
Note 215A: One of ordinary skill in the art would understand that rendering a 3D model in a graphical user interface on a display device requires generating a 2D representation or rendering of the 3D model.
Regarding claim 223:
Golparavar-Fard teaches:
A method for generating a three-dimensional (3D) representation, the method comprising:
receiving a plurality of images associated with real camera poses (Golparavar-Fard: The computer storage may store a 3D BIM of a construction site, anchor images, each depicting a viewpoint having a 3D pose and containing features with known 3D positions [0045]);
generating a segmented point cloud (Golparavar-Fard: The viewer 232 may also provide a tool to segment and clip the point cloud for area-based and volumetric measurements, as illustrated in FIG. 11 [0095]) based on the plurality of images, wherein the point cloud comprises a plurality of points (Golparavar-Fard: executes a reconstruction algorithm using the image and the anchor images to calibrate the image to the BIM and generate an initial 3D point cloud model. [0083]);
selecting calculated real camera poses based on an association with a target camera image (Golparavar-Fard: the subset of anchor images may be identified by the date at which the anchor images were photographed (e.g., using the most recent photos), or by matching features in the target images to the calibrated, anchor images. [0074]; see Note 1A), wherein the selected calculated real camera poses real cameras comprise a subset of the plurality of calculated real camera poses real cameras; and
generating a 3D representation comprising a subset of the plurality of points of the point cloud (Golparavar-Fard: The processing device may further display a visual instantiation of the updated 3D point cloud model of the construction site in a graphical user interface of a display device, wherein the visual instantiation of the updated 3D point cloud model includes 3D points [0047]) from a perspective of the virtual camera pose (Golparavar-Fard: The system 100 may provide a user the ability to view the images together with the point cloud from any camera viewpoint, as illustrated in FIGS. 9A and 9B. [0093]).
Golparavar-Fard fails to teach:
segmenting each image of the plurality of images based on a subject of interest in the plurality of images;
selecting calculated real camera poses based on an association with a virtual camera pose
generating a 3D representation comprising a subset of the plurality of points of the point cloud from a perspective of the virtual camera pose based on a relation of the virtual camera pose to the selected calculated real camera poses.
Narayanan teaches:
segmenting each image of the plurality of images based on a subject of interest in the plurality of images (Narayanan: It is possible to use this approach to extend chroma-keying, which uses a fixed back ground color to segment a region of interest from a real video stream and then insert it into another video stream, Pg. 11, Section 6.1: Combining real and virtual models; see Note 223A);
selecting calculated real camera poses based on an association with a virtual camera pose (Narayanan: Selecting the reference VSM. θi is the angle between the virtual camera’s line of sight and the line joining the target point with the position Vi of VSM i. The VSM with the smallest angle θi is selected as the reference VSM, Pg. 6, Fig.3).
generating a 3D representation comprising a subset of the plurality of points of the point cloud from a perspective of the virtual camera pose (Narayanan: A VLM is a dynamically selected scene model that contains a hole-free description of the scene from the virtual camera’s viewpoint, Pg. 7, Section 4.3: Rendering using a VLM, par. 1) based on a relation of the virtual camera pose to the selected calculated real camera poses (Narayanan: A variation of this approach could have the renderings of the component VSMs cross-dissolved (i.e., weighted averaged) based on, say, a closeness metric or the distances of the virtual camera from the VSM origins., Pg. 7, Section 4.3: Rendering using a VLM, par. 1).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Narayanan with Golparavar-Fard. Selecting calculated real camera poses based on an association with a virtual camera pose, as in Narayanan, would benefit the Golparavar-Fard teachings by enabling the system to fill gaps in the image caused by obscured or unknown areas not shown in the original reference images.
Regarding claim 224:
Claim 224 is substantially similar to claim 205, and is therefore rejected for similar reasons. Claim 224 contains the following notable differences:
Claim 224 claims a system instead of a method. Golparavar-Fard teaches a system: “The sparse 3D point cloud may then be stored by the system and displayed by the system in the graphical user interface, e.g., the GUI 138” [0078].
Regarding claim 227:
Claim 227 is substantially similar to claim 209, and is therefore rejected for similar reasons. Claim 227 contains the following notable differences:
Claim 227 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 228:
Claim 228 is substantially similar to claim 210, and is therefore rejected for similar reasons. Claim 228 contains the following notable differences:
Claim 228 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 230:
Claim 230 is substantially similar to claim 212, and is therefore rejected for similar reasons. Claim 230 contains the following notable differences:
Claim 230 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 231:
Claim 231 is substantially similar to claim 213, and is therefore rejected for similar reasons. Claim 231 contains the following notable differences:
Claim 231 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 232:
Claim 232 is substantially similar to claim 214, and is therefore rejected for similar reasons. Claim 232 contains the following notable differences:
Claim 232 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 233:
Claim 233 is substantially similar to claim 215, and is therefore rejected for similar reasons. Claim 233 contains the following notable differences:
Claim 233 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 236:
Claim 236 is substantially similar to claim 223, and is therefore rejected for similar reasons. Claim 236 contains the following notable differences:
Claim 236 claims a system instead of a method. Golparavar-Fard teaches a system: “The sparse 3D point cloud may then be stored by the system and displayed by the system in the graphical user interface, e.g., the GUI 138” [0078].
Regarding claim 237:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), wherein the virtual camera pose corresponds to a synthetic camera (Narayanan: The virtual camera could be anywhere, but its position must be specified in terms of epipolar relationships with the input cameras, Pg. 3, Section 2.3.1: Virtual Camera Position).
Claims 206, 207, 225, and 226 are rejected under 35 U.S.C. 103 over Golparavar-Fard (US 20190325089 A1) in view of Narayanan (NPL: Virtual Worlds using Computer Vision) and Sinha (US 20200005486 A1).
Regarding claim 206:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above),
Golparavar-Fard in view of Narayanan fails to teach:
wherein the point cloud is a line cloud.
Sinha teaches:
wherein the point cloud is a line cloud (Sinha: aspects of the present disclosure provide methods for transforming each 3D point of the 3D point cloud into a 3D line to generate a 3D line cloud [0044]).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Sinha with Golparavar-Fard and Narayanan. Selecting calculated real camera poses based on an association with a virtual camera pose, as in Sinha, would benefit the Golparavar-Fard in view of Narayanan teachings by enhancing security of the point cloud data: “a person or algorithm that gains access to this 3D point cloud 400 could potentially infer confidential information about the confidential bike prototype 210 and/or confidential information displayed on whiteboard 224” (Sinha, [0044]).
Regarding claim 207:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above), further comprising:
Golparavar-Fard in view of Narayanan fails to teach:
segmenting the point cloud based on a subject of interest in the plurality of images, wherein the 3D representation comprises a subset of a plurality of points of the segmented point cloud.
Sinha teaches:
segmenting the point cloud (Sinha: a subset of representative 3D points may be selected in the point cloud, [0041]) based on a subject of interest in the plurality of images (see Note 207A), wherein the 3D representation comprises a subset of a plurality of points of the segmented point cloud (Sinha: "The 3D points are associated with feature descriptors extracted from the source images used to construct the map” [0019]; see Note 207B).
Note 207A: Sinha teaches that feature vectors may be determined that determine subjects of interest (“distinguishing features”) in the query images: “feature descriptors may be selectively generated at distinguishing features of the map, such as corners, highly textured areas, etc. In this manner, query features of similar distinguishing features extracted from query images may be more easily matched with corresponding feature descriptors in the 3D map,” [0041].
Note 207B: As cited above, Sinha further teaches that points associated with feature descriptors may be used to create the “map”, or 3D point cloud (“The 3D map is essentially a spatial database containing geometric data. In some examples, the geometric data comprises a plurality of 3D points that form a 3D point cloud reconstruction of the scene” [0019]). Therefore, it would be obvious to one of ordinary skill in the art to include the subset of a plurality of points of the segmented point cloud in the 3D representation.
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Sinha with Golparavar-Fard and Narayanan. Selecting calculated real camera poses based on an association with a virtual camera pose, as in Sinha, would benefit the Golparavar-Fard in view of Narayanan teachings by enhancing security of the point cloud data: “a person or algorithm that gains access to this 3D point cloud 400 could potentially infer confidential information about the confidential bike prototype 210 and/or confidential information displayed on whiteboard 224” (Sinha, [0044]).
Regarding claim 225:
Claim 225 is substantially similar to claim 206, and is therefore rejected for similar reasons. Claim 225 contains the following notable differences:
Claim 225 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 226:
Claim 226 is substantially similar to claim 207, and is therefore rejected for similar reasons. Claim 226 contains the following notable differences:
Claim 226 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Claims 211 and 229 are rejected under 35 U.S.C. 103 over Golparavar-Fard (US 20190325089 A1) in view of Narayanan (NPL: Virtual Worlds using Computer Vision) and Imber: (US 20160042530 A1; from applicant’s IDS).
Regarding claim 211:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above),
Golparavar-Fard in view of Narayanan fails to teach:
wherein selecting the real cameras associated with the virtual camera comprises selecting real cameras of the plurality of real cameras that are nearest neighbors of the virtual camera.
Imber teaches:
wherein selecting the real camera poses associated with the virtual camera pose comprises selecting real camera poses of the plurality of real cameras poses that are nearest neighbors of the virtual camera pose (Imber: "the reference image may be captured from a reference camera viewpoint and it may be compared with images captured from camera viewpoints which are the nearest neighbours of the reference camera viewpoint," [0054]; see Note 211A).
Note 211A: “Comparing reference images that may be captured from a reference camera viewpoint and may be compared with images captured from camera viewpoints which are the nearest neighbours of the reference camera viewpoint” as cited above requires that the viewpoints of the nearest neighbours be identified or “selected” for comparison separately from the reference viewpoint.
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Golparavar-Fard in view of Narayanan with the teachings of Imber. Selecting real camera poses of the plurality of real cameras that are nearest neighbors of the virtual camera pose, as in Imber, would benefit the Golparavar-Fard in view of Narayanan teachings, because Imber teaches: “the nearest neighbours are used because they are likely to have visibility of the same sample positions on the geometry as the reference image, and also because this is likely to reduce the extent to which the different camera viewpoints will have glancing views of the surfaces of the geometry in the scene,” [0054].
Regarding claim 229:
Claim 229 is substantially similar to claim 211, and is therefore rejected for similar reasons. Claim 229 contains the following notable differences:
Claim 229 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Claims 216, 217, 234, and 235 are rejected under 35 U.S.C. 103 over Golparavar-Fard (US 20190325089 A1) in view of Narayanan (NPL: Virtual Worlds using Computer Vision) and Quan (US 20100201682 A1).
Regarding claim 216:
Golparavar-Fard in view of Narayanan teaches:
The method of claim 205 (as shown above),
Golparavar-Fard in view of Narayanan fails to teach:
wherein generating the 3D representation comprising the subset of the plurality of points of the point cloud from the perspective of the virtual camera pose comprises generating a color value for each point of the subset of the plurality of points.
Quan teaches:
wherein generating the 3D representation comprising the subset of the plurality of points of the point cloud from the perspective of the virtual camera pose comprises generating a color value for each point of the subset of the plurality of points (Quan: "it can be assumed that point X is visible from most of the cameras. Under this assumption, c ≈ mediank {ck}." [0062]; see Note 216A).
Note 216A: Quan teaches that a color may be retrieved for every point in a point cloud: “Consider a 3D point X=(x, y, z, 1)' with color c,” [0062]. Quan further teaches: “if the point is occluded by some other objects in this camera, the color of the projection is usually not the same as c,” [0062]. Quan then estimates the color c based on the equation cited above.
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Quan with Golparavar-Fard and Narayanan. Generating a color value for each point of the subset of the plurality of points, as in Quan, would benefit the Golparavar-Fard in view of Narayanan teachings by ensuring that the correct color is rendered when aggregating views from multiple viewpoints.
Regarding claim 217:
Golparavar-Fard in view of Narayanan and Quan teaches:
The method of claim 216 (as shown above), wherein generating the color value for each point of the subset of the plurality of points comprises selecting a predominant color value of pixel color values (Quan: “c ≈ mediank {ck}." [0062]; see Note 217A) according to the images that were used to triangulate the point (Quan: Assuming that point X can be visible from multiple cameras, I={Pi}, and occluded by some objects in the other cameras, I'={Pj}, then the color, ci, of the projections in I should be the same as c, while it may be different from the color, cj, of projections in I, [0062]; see Note 217B).
Note 217A: Quan teaches that ck is the set of projection colors: “Given a set of projection colors, {ck},” [0062]. Quan further teaches that the median of this set is chosen as a predominant color value, as cited above in [0062].
Note 217B: Singh (US 20180276885 A1) explains the definition of triangulation when used as a term of art: “If the cameras and their positions in space are known, it is possible to reconstruct 3D points of the observed object directly. This can be achieved by intersecting the rays from the camera centers through the feature points of one particular correspondence. This technique is called triangulation.” [0005]. Therefore, the Examiner understands that one of ordinary skill in the art would understand Quan to be triangulating the point X in order to obtain the color c.
Regarding claim 234:
Claim 234 is substantially similar to claim 216, and is therefore rejected for similar reasons. Claim 234 contains the following notable differences:
Claim 234 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Regarding claim 235:
Claim 235 is substantially similar to claim 217, and is therefore rejected for similar reasons. Claim 235 contains the following notable differences:
Claim 235 claims a system instead of a method. In the rejection of claim 224, it was shown that Golparavar-Fard teaches a system.
Conclusion
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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