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 .
DETAILED ACTION
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 12254563 B2.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the Patent US 12254563 B2 contains substantially all the limitations of the instant application claims.
Application No. 19051296
Patent No. US 12254563 B2
1. A method of generating a two-dimensional orthographic map of an environment,-comprising: capturing, with a camera integrated with an augmented reality (AR) framework, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes; annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surface planes; determining, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes; estimating offsets for each of the surface planes relative to the projection plane; rendering, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes defined by the offsets, a two- dimensional orthographic map that models the plurality of objects; and extracting and outputting a real-world measurement based on a selection of pixels on the orthographic map.
1. A method of modeling an interior environment, comprising:
capturing, with a camera in communication with an augmented reality (AR) engine, at least one image of the interior environment, wherein the interior environment comprises a plurality of objects, each object having a surface oriented along a corresponding surface plane, wherein the surface planes are parallel to each other;
annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of at least one object of the plurality of objects in at least one of the surface planes;
determining, based on the plurality of orientation lines and information received from the AR engine, an orientation of a projection plane that is parallel to each of surface planes, wherein the projection plane is representative of a real-world size of the interior environment;
estimating an offset for each of the surface planes relative to the projection plane; and
rendering, based on the offset for each of the surface planes, a two-dimensional orthographic map that models the interior environment; and
constructing a real-world projection plane normal N={cos(θopt), 0, sin(θopt)} that specifies the projection plane, and wherein the rendering includes generating a plane having a local origin defined by an intersection plane with of the real-world projection plane normal N.
12. A system for generating a two-dimensional orthographic map of an environment, comprising:a camera configured to capture images;an augmented reality (AR) framework configured to analyze captured images and provide spatial data, including positions and orientations of objects;a processor; andmemory in communication with the processor and storing instructions that cause the processor to:capture, with the camera, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes;annotate the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surfaceplanes;determine, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes;estimate offsets for each of the surface planes relative to the projection plane; andrender, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes defined by the offsets, a two- dimensional orthographic map that models the plurality of objects.
12. A system, comprising:
a computing device, comprising:
a processor,
memory in communication with the processor;
a camera configured to capture digital images and store them in the memory;
one or more position sensors; and
an augmented reality (AR) framework executed by the computing device and in communication with the one or more position sensors and the camera;
wherein the computing device is configured to:
capture, with the camera, at least one image of an interior environment, wherein the interior environment comprises a plurality of objects, each object having a surface oriented along a corresponding surface plane, and wherein the surface planes are parallel to each other;
automatically annotate the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of at least one object of the plurality of objects in at least one of the surface planes;
determine, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes, wherein the projection plane is representative of a real-world size of the interior environment;
estimate an offset for each of the surface planes relative to the projection plane; and
render, based on the offset for each of the surface planes, a two-dimensional orthographic map that models the interior environment; and
construct a real-world projection plane normal N={cos(θopt), 0, sin(θopt)} that specifies the projection plane, and wherein the rendering includes generating a plane having a local origin defined by an intersection plane with of the real-world projection plane normal N.
20. A non-transitory computer-readable medium storing instructions, which, when executed by a processor of a computing device, cause the computing device perform a method of: capturing, with a camera integrated with an augmented reality (AR) framework, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes; annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surface planes; determining, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes; estimating offsets for each of the surface planes relative to the projection plane; and rendering, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes defined by the offsets, a two- dimensional orthographic map that models the plurality of objects.
19. A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:
capturing, with a camera in communication with an augmented reality (AR) engine, at least one image of an interior environment, wherein the interior environment comprises a plurality of objects, each object having a surface oriented along a corresponding surface plane, wherein the surface planes are parallel to each other;
annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of at least one object of the plurality of objects in at least one of the surface planes;
determining, based on the plurality of orientation lines and information received from the AR engine, an orientation of a projection plane that is parallel to each of surface planes, wherein the projection plane is representative of a real-world size of the interior environment;
estimating an offset for each of the surface planes relative to the projection plane; and
rendering, based on the offset for each of the surface planes, a two-dimensional orthographic map that models the interior environment; and
constructing a real-world projection plane normal N={cos(θopt), 0, sin(θopt)} that specifies the projection plane, and wherein the rendering includes generating a plane having a local origin defined by an intersection plane with of the real-world projection plane normal N, wherein θopt is an optimum rotation angle of a test plane that intersects with a pivot point such that the test plane matches the projection plane.
Dependent claims 2-11, 13-19 recite similar matters as claim 2-11, 13-18 of Patent US 12254563 B2 and are rejected for the same reason.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 4-14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hoiem (US Pub 20210183080 A1) in view of Liu, Chen, et al. ("Planercnn: 3d plane detection and reconstruction from a single image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.).
As to claim 1, Hoiem discloses a method of generating a two-dimensional orthographic map of an environment (Hoiem, ¶0011, “the 3D point cloud may be converted to an as-built drawing by orthographically projecting a top-down view of the vertical planes onto the ground plane, producing a series of lines that correspond to potential walls.” ¶0065, “An orthographic map (50) of the scene may also be created (52) and viewed on its own, or viewed with the plan drawing using the same alignment as for the as-built drawing.”) comprising:
capturing, with a camera integrated with an augmented reality (AR) framework, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes (Hoiem, ¶0011, “If the site plan is a 2D drawing, the 3D point cloud may be converted to an as-built drawing by orthographically projecting a top-down view of the vertical planes onto the ground plane, producing a series of lines that correspond to potential walls. Endpoints for these lines are determined as the extent of 3D points that are assigned to the plane, and each 3D point that corresponds to the ground or a wall is plotted in proportion to its 2D coordinate on the ground plane. The as-built drawing is thus aligned to the plan drawing by solving for a rotation angle, 2D translation, and 1D scale, such that the transformed as-built drawing wall points are near the wall lines with the same orientation in the plan drawing.” ¶0033, “vertical planes are extracted, either from the scene points (i.e., a “point cloud”) reconstructed during SfM or from points extracted in an optional multiview stereo step. These vertical planes can be represented as lines on a floor map. To facilitate alignment of the scene to an architectural drawing or 3D BIM, lines are extracted from such a “plan.” In the case of a drawing, extraneous markings and short disconnected lines are, preferably, first removed. In the case of BIM, the vertical planes can be represented as lines on a floor map.” ¶0043, “as-built conditions of a building interior or exterior, defects and areas in need of repair, progress of construction, or layout of pipes and electrical work”);
annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surface planes (Hoiem, ¶0033, “Thereafter, the vertical direction is determined, e.g., based on the scene geometry and/or the orientation information contained in the image metadata, and vertical planes are extracted, either from the scene points (i.e., a “point cloud”) reconstructed during SfM or from points extracted in an optional multiview stereo step. These vertical planes can be represented as lines on a floor map. To facilitate alignment of the scene to an architectural drawing or 3D BIM, lines are extracted from such a “plan.” In the case of a drawing, extraneous markings and short disconnected lines are, preferably, first removed. In the case of BIM, the vertical planes can be represented as lines on a floor map. Then, the extracted lines from the point cloud are aligned to the lines from the drawing or BIM.”);
determining, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes (Hoiem, ¶0067, “3D planes can be fit to the 3D points, for example using RANSAC, by sampling a small number of points (typically 3), fitting a plane to them, determining the goodness of fit of each other point to the plane based on position and/or normal, and determining a subset of points that are sufficiently good fits. The ground plane orientation can be determined as the orientation of plane(s) that are approximately horizontal, or by assuming that the largest plane (either in area or most points assigned) corresponds to the ground. Once the ground orientation is determined, planes can be extracted from the point cloud that are perpendicular to the ground, and these planes serve as wall candidates for aligning to drawings or a 3D plan model.” ¶0068, “If aligning to drawings, the 3D point cloud is first converted to an “as-built drawing” by orthographically projecting a top-down view of the vertical planes onto the ground plane, producing a series of lines that correspond to potential walls.” ¶0074, “To localize to a 3D CAD or BIM model, horizontal and vertical planes can be extracted from the 3D plan model. The 3D plan model can then be aligned to the horizontal and vertical planes from the point cloud using point-to-plane ICP. Initialization can be performed based on the drawing alignment, with multiple randomized initializations, or by proposing candidates based on triplets of orthogonal intersecting planes (i.e., corners produced by the intersections of three planes). A point-to-plane ICP method may also be used for 3D alignment.”);
rendering, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes (Hoiem, ¶0011, “the 3D point cloud may be converted to an as-built drawing by orthographically projecting a top-down view of the vertical planes onto the ground plane” ¶0030, “as-built drawings, orthographic colored maps, and/or 3D models of the scene may be projected or overlaid on the drawing and/or BIM.” ¶0065, “An orthographic map (50) of the scene may also be created (52) and viewed on its own, or viewed with the plan drawing using the same alignment as for the as-built drawing.” ¶0068, “If aligning to drawings, the 3D point cloud is first converted to an “as-built drawing” by orthographically projecting a top-down view of the vertical planes onto the ground plane, producing a series of lines that correspond to potential walls.”); and
extracting and outputting a real-world measurement based on a selection of pixels on the orthographic map (Hoiem, ¶0042, “a 3D position and orientation, a pixel position and scale value, or an orientation and distance.” ¶0065, “An orthographic map (50) of the scene may also be created (52) and viewed on its own, or viewed with the plan drawing using the same alignment as for the as-built drawing.” ¶0071, “Given the orientation θ, the problem is restricted to finding one scale s and two translations tx and ty.” ¶0076, “producing a map or model that can be used to inspect and document site conditions”).
Hoiem does not explicitly disclose “estimating an offset for each of the surface planes relative to the projection plane” and “defined by the offsets”.
Liu teaches “estimating an offset for each of the surface planes relative to the projection plane” (Liu, Page 4452, “use a simple algebraic formula (Eq. 1) to calculate the plane offset (which is differentiable for end-to-end training).” “Plane offset estimation:”);
“defined by the offsets” (Liu, Page 4452-4453, “Plane offset estimation:”).
Hoiem and Liu are considered to be analogous art because all pertain to reconstruction of planar surface. It would have been obvious before the effective filing date of the claimed invention to have modified Hoiem with the features of “estimating an offset for each of the surface planes relative to the projection plane” and “defined by the offsets”. as taught by Liu. The suggestion/motivation would have been in order to more effectively infers piecewise planar structure from a single RGB image (Liu, page 4450).
As to claim 2, claim 1 is incorporated and the combination of Hoiem and Liu discloses the rendering includes generating a plane having a local origin defined by an intersection plane with a real-world projection plane normal N (Hoiem, ¶0066, “Following the SfM process (and any optional multiview stereo methods and meshing), an as-built drawing can be created and localized to the plan drawing or BIM 24.” ¶0067, “The first step for alignment is to determine the gravity direction relative to the camera poses and points of the 3D point cloud (“3D points”). The images and 3D points can then be rotated so that the “up” direction is in the same linear direction as gravity. The gravity direction may be determined based on metadata contained in the images, such as data from an accelerometer or gyroscope, and/or by fitting 3D planes to the 3D points. Often, cameras record an approximate world orientation of the camera's pose at the time each image is taken. In such cases, an average of estimates of gravity directions can be used to align the camera poses and 3D points so their up vector is consistent with gravity. Further, 3D planes can be fit to the 3D points, for example using RANSAC, by sampling a small number of points (typically 3), fitting a plane to them, determining the goodness of fit of each other point to the plane based on position and/or normal, and determining a subset of points that are sufficiently good fits. The ground plane orientation can be determined as the orientation of plane(s) that are approximately horizontal, or by assuming that the largest plane (either in area or most points assigned) corresponds to the ground. Once the ground orientation is determined, planes can be extracted from the point cloud that are perpendicular to the ground, and these planes serve as wall candidates for aligning to drawings or a 3D plan model.” ¶0072, “fter orienting each drawing so that lines are in the vertical and horizontal directions”).
As to claim 4, claim 1 is incorporated and the combination of Hoiem and Liu discloses the annotating is performed using a deep-learning neural network trained to detect and recognize planar features in perspective images (Liu, abstract).
As to claim 5, claim 1 is incorporated and the combination of Hoiem and Liu discloses generating a point cloud of the environment based on AR framework data; and utilizing the point cloud to estimate the offsets for the parallel surface planes (Liu, Page 4452, “use a simple algebraic formula (Eq. 1) to calculate the plane offset (which is differentiable for end-to-end training).” “Plane offset estimation:”).
As to claim 6, claim 1 is incorporated and the combination of Hoiem and Liu discloses capturing the at least one image comprises utilizing the ARframework to automatically capture relative positions and orientations of the camera in a world coordinate system while the at least one image is captured (Liu, abstract. Liu, Page 4451, 3D scans. Page 4452.Plane parameters.).
As to claim 7, claim 1 is incorporated and the combination of Hoiem and Liu discloses the rendering comprises using a scale factor for the orthographic map to convert pixel distances of the orthographic map to real-world measurements (Hoiem, ¶0042, “a 3D position and orientation, a pixel position and scale value, or an orientation and distance.” ¶0071, “Given the orientation θ, the problem is restricted to finding one scale s and two translations tx and ty.”).
As to claim 8, claim 7 is incorporated and the combination of Hoiem and Liu discloses extracting the real-world measurement comprises selecting pixel-based dimensions on the orthographic map and applying the scale factor (Hoiem,¶0033, “To refine the alignment, we solve for scales, 2D rotations, and translations that best aligns the lines from the point cloud to the lines from the plan, such that each line in the point cloud is aligned to each line in the plan” ¶0042, “a 3D position and orientation, a pixel position and scale value, or an orientation and distance.”.
As to claim 9, claim 1 is incorporated and the combination of Hoiem and Liu discloses each of the plurality of orientation lines are horizontal lines (Hoiem, ¶0071, “With the assumption of two dominant orientations, this means the alignment is given by sampling two lines at one orientation and one line at the other orientation in each drawing and specifying one of four angles (0, 90, 180, or 270 degrees) that relates an orientation in one drawing to the other. Given the orientation θ, the problem is restricted to finding one scale s and two translations tx and ty. We can consider the as-built drawing to have two ordered lists of 1D points xi B∈XB and yi B∈YB, which correspond to the sorted positions of the lines at each orientation. Likewise, for the plan drawing, there are two ordered lists xi P∈XP and yi P∈YP.”).
As to claim 10, claim 9 is incorporated and the combination of Hoiem and Liu discloses the determining the orientation of the projection plane comprises using a numerical optimizer to minimize differences in coordinates of endpoints of the plurality of orientation lines. wherein the determining the orientation of the projection plane comprises using a numerical optimizer to minimize differences in Y-coordinates of endpoints of the horizontal lines (Hoiem, ¶0061, “Bundle adjustment, as employed in accordance with the present invention, is a gradient descent optimization procedure to a gradient descent optimization procedure to minimize the reprojection error between estimated 3D points and their observations in the images, where the reprojection depends on the tracks (encoding the observations for each 3D point), the pose of the camera for each image (encoding the translation and rotation of each camera within the same 3D coordinate system), and the intrinsic parameters of the camera.” “The bundle adjustment may, for example, solve for the 3D point positions and camera poses that minimize a Huber loss on the distance between reprojected points and their observed feature positions. It may be desirable to iteratively perform bundle adjustment and re-triangulate the feature tracks based on the observation and camera pose, with suitable outlier checks such as minimum angle and reprojection error. After this step, the camera poses are precisely estimated, and a number of 3D points are estimated that correspond to scene surfaces.”).
As to claim 11, claim 1 is incorporated and the combination of Hoiem and Liu discloses generating, with the AR framework, a pivot point in a common visible position of each image of the at least one image and determining an optimum rotation angle of a test plane that intersects with the pivot point such that the test plane matches the projection plane (Hoiem, ¶0072, “FIG. 10 is an illustration of this as-built to plan drawing alignment. Detected lines in the two main orthogonal directions are represented as 1D points corresponding to their positions. After orienting each drawing so that lines are in the vertical and horizontal directions, for each 90 degree rotation, translation and scale can be determined by solving for two pairs of corresponding points in each direction that yield similar scale estimates.”).
As to claim 12, the combination of Hoiem and Liu discloses a system for generating a two-dimensional orthographic map of an environment, comprising:a camera configured to capture images;an augmented reality (AR) framework configured to analyze captured images and provide spatial data, including positions and orientations of objects;a processor; andmemory in communication with the processor and storing instructions that cause the processor to:capture, with the camera, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes;annotate the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surfaceplanes;determine, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes;estimate offsets for each of the surface planes relative to the projection plane; andrender, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes defined by the offsets, a two- dimensional orthographic map that models the plurality of objects (See claim 1 for detailed analysis.).
As to claim 13, claim 12 is incorporated and the combination of Hoiem and Liu discloses the instructions further cause the processor to extract and output a real-world measurement based on a selection of pixels on the orthographic map (See claim 1 for detailed analysis.).
As to claim 14, claim 12 is incorporated and the combination of Hoiem and Liu discloses the instructions further cause the processor to generate a plane having a local origin defined by an intersection plane with a real-world projection plane normal N to render the two-dimensional orthographic map (See claim 2 for detailed analysis.).
As to claim 16, claim 12 is incorporated and the combination of Hoiem and Liu discloses a deep-learning neural network trained to detect and recognize planar features in perspective images to annotate the at least one image with the plurality of orientation lines (See claim 4 for detailed analysis.).
As to claim 17, claim 12 is incorporated and the combination of Hoiem and Liu discloses the instructions further cause the processor to:generate a point cloud of the environment based on AR framework data; andutilize the point cloud to estimate the offsets for the parallel surface planes (See claim 5 for detailed analysis.).
As to claim 18, claim 12 is incorporated and the combination of Hoiem and Liu discloses the AR framework is configured to automatically capture relative positions and orientations of the camera in a world coordinate system while the at least one image is captured (See claim 6 for detailed analysis.).
As to claim 19, claim 12 is incorporated and the combination of Hoiem and Liu discloses the instructions further cause the processor to apply a scale factor to convert pixel distances of the orthographic map to real-world measurements (See claim 7 for detailed analysis.).
As to claim 20, the combination of Hoiem and Liu discloses a non-transitory computer-readable medium storing instructions, which, when executed by a processor of a computing device, cause the computing device perform a method of:capturing, with a camera integrated with an augmented reality (AR) framework, at least one image of a plurality of objects, each object having a surface aligned along parallel surface planes;annotating the at least one image with a plurality of orientation lines, each of the orientation lines corresponding to an edge of the surface aligned along the parallel surface planes;determining, based on the plurality of orientation lines and information received from the AR framework, an orientation of a projection plane that is parallel to each of the surface planes;estimating offsets for each of the surface planes relative to the projection plane; andrendering, by projecting vertices of the orientation lines and corresponding portions of each object surface onto corresponding rendering planes defined by the offsets, a two- dimensional orthographic map that models the plurality of objects (See claim 1 for detailed analysis.).
Allowable Subject Matter
Claims 3 and 15 are 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, and overcome the double patenting rejection.
3. The method of claim 2, wherein the real-world projection plane normal N = {cos(θopt), 0, sin(θopt)}, wherein θopt is an optimum rotation angle of a test plane that intersects with a pivot point such that the test plane matches the projection plane.
15. The system of claim 14, wherein the real-world projection plane normal N = {cos(θopt), 0,sin(θopt)}, wherein θopt is an optimum rotation angle of a test plane that intersects with a pivot point such that the test plane matches the projection plane.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Belle et al. (US Pub 2021/0166495 A1) teaches capturing and aligning multiple 3D scenes.
Jovanovic et al. (US Pub 2020/0357132 A10 teaches capture at least one photo of the space during the active AR session; extracting camera data from the AR session for the at least one photo; extracting the backing model from the AR session.
Myllykoski (US Pub 2015/0369593 A1) teaches creating accurate physical measurement data from digital pictures.
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/YU CHEN/Primary Examiner, Art Unit 2613
