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 .
Priority
The present application is a 371 of international PCT/SG2023/050173 filed on 03/17/2023 and claims benefit of foreign application DE 10 2022 203 367.1 filed on 04/05/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 09/27/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-2 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), and in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”).
Regarding claims 1 and 9,
Zhao teaches: A method of calibrating a depth map generating system comprising a projection system and an imaging system (Zhao teaches “an apparatus and a method for detecting deformation of a structured light depth imaging system and automatic re-calibration for the depth imaging system…a depth imaging device includes a light projector, a camera and a processor” and further discloses “the processor detects a misalignment between the orientation between the light projector and the camera based on a relationship between the features in the projected pattern image and the features in the reflected pattern image” (Abstract; ¶ [0004]).), the method comprising:
[claim 9: A depth map generating system comprising a projection system and an imaging system, and a controller, the controller being configured to (Zhao teaches a depth imaging device, including a “light projector, a camera and a processor” (Abstract; ¶ [0004]-[0005]), wherein the processor performs the calibration operations disclosed throughout Zhao. Under the broadest reasonable interpretation, Zhao’s processor corresponds to the claimed controller because it is configured to control the projection system and imaging system and execute the recited calibration functions.):
using the projection system to project an array of dots onto a planar surface located at a (Abstract “The light projector emits light corresponding to a projected pattern image having a plurality of features with known locations in the projected pattern image.”; ¶ [0033] “The speckle pattern 400 (also referred to as dot pattern or pattern of dots”; ¶ [0035] “The depth imaging system includes an optical projector to project the dot pattern onto a scene.”; Under the broadest reasonable interpretation, the scene taught by Zhao includes one or more planar surfaces located at a distance from the projection system; however, Zhao does not expressly disclose the planar surface is located at a first known distance.), obtaining a first image of the projected dots using the imaging system (¶ [0035] “The light is reflected by the scene and captured by a sensing camera.”; ¶ [0027] “The depth camera 34 captures the reflected light”; Abstract), and determining a first set of three- dimensional positions of the projected dots at the planar surface (¶ [0064] “The 3D positions of pattern dots projected onto the environment in the 3D world are
X
i
,”);
using the projection system to project an array of dots onto a (Zhao’s teachings with respect to the previous limitation mirror the teachings applicable to this limitation. Zhao does not use a second image at a second known distance.);
(Zhao teaches projection lines defined by corresponding projected points and the optical center (¶ [0039] “ The projection line for projection point 515 is the projection line 517, which connects the optical center point 512 and the projection point 515”; ¶ [0050]); and
using the (Zhao teaches automatically generating updated extrinsic calibration parameters (R’, T’) representative of the geometry of the projection system (¶ [0060]-[0066]). Zhao further teaches that the projector has an optical center (¶ [0038]; ¶ [0063]) and uses the updated projector geometry together with projected dot correspondences and intrinsic parameters (i.e. “
K
1
and
K
2
…(e.g., focal length, image sensor format, principal point)” ¶ [0061]) to perform triangulation and calculate 3D positions of projected dots (¶ [0061]-[0067). Thus, under the broadest reasonable interpretation, Zhao teaches using the center position of the projection system, together with the projected dots and intrinsic properties of the imaging system, to determine the spatial positions of projected dots.).
Zhao fails to explicitly disclose: projecting an array of dots onto first and second planar surfaces (i.e. using first and second planar calibration surfaces) located at first and second known distances from the projection system, associating corresponding three-dimensional projected dots obtained from the first and second planar surfaces, using convergence of lines defined by the associated projected dots to calculate the center position of the projection system, and using the calculated center position to predict the spatial positions of projected dots for different object distances.
In a related art, Huang teaches: calibrating a camera-projector system using a planar calibration board at multiple calibration board poses, wherein “the projector projects encoded SL patterns onto the calibration board, and these patterns are then captured by the camera for calibrating the projector”, “pixel correspondences between camera and projector can be established by matching the captured and projected patterns. In the end, the 3D coordinates of the deformed pattern pixels are triangulated, given the camera-projector parameters and pixel correspondences,” the “camera-projector framework requires correspondences between the projector image plane and a reference plane, which is usually approximated by a planar calibration board,” and “requires only one shot of SL pattern for each calibration board pose” (Abstract; p. 15 Introduction section paragraphs 1-4). Under the broadest reasonable interpretation, each calibration board pose represents a different instance of the planar reference surface relative to the fixed camera-projector system. Accordingly, the multiple calibration board poses correspond to first and second planar surfaces located at first and second known distances from the projection system, thereby teaching projecting structured light patterns onto first and second planar surfaces, obtaining corresponding calibration images, and determining three-dimensional positions of the projected pattern for calibration.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Huang’s planar calibration methodology in the projector-camera calibration process and system of Zhao because Huang teaches that using structured-light feature points acquired from multiple planar calibration board poses together with bundle adjustment “improves calibration robustness” by boosting “both the number of feature points and their spatial distribution” (see Huang p. 16, left column lines 5-8), thereby predictably improving the accuracy and subsequent three-dimensional reconstruction.
Zhao in view of Huang fails to explicitly disclose: using convergence of lines defined by the associated projected dots to calculate the center position of the projection system, and using the calculated center position to predict the spatial positions of projected dots for different object distances.
In a related art, Grossberg teaches: associating projected dots of the first set of three-dimensional positions with projected dots of the second set of three-dimensional positions that define lines (Grossberg teaches that “A ray corresponding to a pixel i intersects two plane, separated by a known distance z, at points
p
n
and
p
f
”, and “The two-plane method densely recovers the image plane to scene ray correspondence,” wherein “The two points
p
n
,
p
f
determine the ray” (see Figure 10 and p. 130 left column, lines 4-19). Thus, Grossberg teaches corresponding points recovered on two planes define a common ray (i.e. defined line).) and using convergence of the lines to calculate a position of the center of the projection system (Grossberg teaches determining rays from corresponding projected features observed on two known planes, wherein “The two points
p
n
,
p
f
determine the ray and thus the direction of the ray
q
f
”, and further teaches that the recovered rays are used to compute caustic (see p. 130 Figure 10 and p. 130 left column, lines 4-22), and that for an imaging system “the caustic of the perspective system is a single point,” corresponding to the center of projection (see p. 126 right column, lines 7-21 and Figure 7 and it’s description).);
and using the calculated center position of the projection system, (Grossberg teaches recovering the ray geometry of the imaging system from corresponding points on two known planes, computing the caustic of the recovered rays (refer to previous Grossberg teachings above, and p. 130 Figure 10 and p. 130 left column, lines 4-22, and p. 126 right column, lines 7-21 and Figure 7)), and computing the caustic (i.e. center of projection) of the recovered rays. Grossberg further teaches representing each recovered ray by p(x, y,r) = p(x, y) + rq(x, y), wherein “We can express the position of any point along the ray as p(x, y,r) = p(x, y) + rq(x, y)” (see p. 122 Section 2.1, p.124 right column Geometric Model 2 section). Thus, the spatial position of a point along the recovered ray is determined as a function of the distance parameter r (distance is taught as r on p. 128 section 4.1). Under the broadest reasonable interpretation, this teaches predicting the spatial position of dots of light for different distances of an object from the imaging system.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further utilize the ray-based calibration methodologies of Grossberg in the projector-camera calibration of Zhao, as modified by Huang, because Grossberg teaches recovering the ray geometry of an imaging system from corresponding observations on multiple planes, from which the center of projection for a perspective imaging system is determined, thereby enabling accurate determination of spatial positions along the recovered rays for different distances. Doing so would have predictably improved the geometric accuracy of Zhao’s projector-camera calibration and subsequent depth mapping and three-dimensional reconstruction. All three references are directed to the calibration of camera-projector imaging systems used for three-dimensional reconstruction.
Regarding claims 2 and 10,
Zhao, in view of Huang and Grossberg, teach: the method of claim 1 and the depth map generating system of claim 9, wherein associating the projected dots of the first set of three-dimensional positions with the projected dots of the second set of three- dimensional positions (Taught by Zhao, Huang and Grossberg with respect to claims 2 and 10.) comprises:
Grossberg further teaches: calculating lines which extend from an (Grossberg teaches “The two points
p
n
,
p
f
determine the ray and thus the direction of the ray” and that “we can determine the caustic for the caustic raxel model by the Jacobian method described in Section 3.2.” (p. 130 left column, lines 4-19, and Figure 10), and that for a perspective imaging system, “All rays entering the imaging systems intersect at a single point called the center of projection (COP) or viewpoint” (p. 119 Introduction section). In summary, Grossberg teaches recovering rays from corresponding observations on two calibration planes and recovering the center of projection from the recovered ray geometry. Under the broadest reasonable interpretation, the recovered center of projection corresponds to the claimed estimated center of the projection system, the recovered rays correspond to the claimed calculated lines extending through the first set of three-dimensional positions, and the intersections of those recovered rays with the second calibration plane correspond to the claimed intersection points.).
Although Zhao, Huang and Grossberg fail to explicitly disclose: calculating lines which extend from an estimated center of the projection system through the first set of three-dimensional positions of the projected dots, determining intersection points of these lines with the plane of the second set of three-dimensional positions of the projected dots, and associating the intersection points with the dots of the second set of the projected dots, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the further teachings of Grossberg to the combined calibration framework of Zhao, previously modified by Huang and Grossberg, because Grossberg teaches recovering rays extending between corresponding calibration observation on multiple planar calibration surfaces. Applying Grossberg’s further teachings of ray-based geometry to the calibration process of Zhao, Huang, and Grossberg would have predictably enabled calculating lines extending through corresponding three-dimensional projected positions, determining their intersections with another calibration plane, and establishing corresponding projected points, thereby improving the geometric accuracy and robustness of the projector-camera calibration.
Claims 3, 7, 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), and in further view of Tubic et al. (US 20160350929 A1; hereinafter “Tubic”).
Regarding claims 3 and 11,
Zhao, in view of Huang and Grossberg, teach: the method of claim 2 and the depth map generating system of claim 10, including determining intersection points of these lines with the plane of the second set of three-dimensional positions of the projected dots, and associating the intersection points with the dots of the second set of the projected dots.
Zhao, Huang, and Grossberg fail to explicitly disclose: wherein a distance between an intersection point of a line and a dot of the second set of dots is calculated, and if that distance exceeds a threshold value then the dot is rejected and is not associated with a dot of the first set of dots.
In a related art, Tubic teaches: calculating a distance between intersecting rays for evaluating a candidate match, where “FIGS. 7A and 7B illustrate two example error measurements that can be attributed to an intersection,” including “ the error measure 702 is the distance between the intersection of the two camera rays and the projector ray” (¶ [0051]). Tubic further teaches applying a threshold to the calculated values to validate or reject candidate matches because “The number of plausible combinations can be reduced significantly after imposing a threshold to the obtained values” and “The average error can be further validated after applying a threshold,” where ambiguous matches are rejected (¶ [0051]-[0053]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Tubic’s validation techniques to the combined calibration framework of Zhao, Huang, and Grossberg because Tubic teaches using a calculated distance and a threshold to determine whether a candidate match should be accepted or rejected. Applying Tubic’s validation technique to the projected dot associations determined by Zhao, Huang, and Grossberg (See corresponding teachings in claims 1, 3, 9, and 11 rejections) would have predictably improved the accuracy of the projected dot associations by rejecting unreliable/erroneous correspondences before calibration. All four references are directed to improving the accuracy of structured light projector-camera calibration and three-dimensional reconstruction.
Regarding claims 7 and 15,
Zhao, in view of Huang and Grossberg, teach: the method of claim 1 and the depth map generating system of claim 9, including intrinsic properties of the imaging system.
Zhao, in view of Huang and Grossberg, fail to explicitly disclose: wherein the method further comprises measuring intrinsic properties of the imaging system.
In a related art, Tubic teaches: measuring intrinsic properties of the imaging system (¶ [0044] “The cameras 110 and the light projector unit 130 are calibrated in a common coordinate system. This means that intrinsic parameters… are measured”.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the intrinsic measuring techniques taught by Tubic to the intrinsic properties of the imaging system taught by Zhao, Huang, and Grossberg in order to obtain more accurate intrinsic properties for the imaging system, thereby increasing the accuracy of subsequent projection processes.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), and in further view of Fan et al. (CN 101949768 B; hereinafter “Fan”; translated copy provided by Examiner).
Regarding claims 4 and 12,
Zhao, in view of Huang and Grossberg, teach: the method of claim 1 and the depth map generating system of claim 9, including a first image of the projected dots.
Zhao, in view of Huang and Grossberg, fail to explicitly disclose: wherein centers of dots of the first image of the projected dots are calculated using quadratic interpolation.
In a related art, Fan teaches: wherein centers of dots (Fan teaches calculating the sub-pixel location (i.e. center) of an imaged light spot by finding the maximum correlation position between a reference template and the spot image and performing quadratic interpolation to determine the spot offset (¶ [0070] “The correlation function value is the largest at the position where the reference template coincides with the sub-aperture spot, and the offset of the sub-aperture spot is calculated… obtains the sub-pixel level offset of the
light spot of each sub-aperture through a quadratic interpolation operation”; ¶ [0085] “find the maximum correlation function value within the effective range of each sub-aperture, and to latch the position of the maximum correlation function value”; ¶ [0086] “the spot offset … is obtained in the interpolation module.”). Under the broadest reasonable interpretation, determining the sub-pixel location of an imaged light spot corresponds to calculating the center of a dot.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the Zhao, Huang, and Grossberg combination to calculate the centers of the projected dots taught by Zhao, Huang, and Grossberg using the quadratic interpolation technique taught by Fran because Fan teaches determining the sub-pixel location of a detected light spot by quadratic interpolation after identifying the correlation maximum, thereby improving localization accuracy. A person of ordinary skill in the art would have recognized that this known sub-pixel interpolation technique could be applied to the projected dots of Zhao, Huang, and Grossberg to improve the accuracy of calculating their centers, with predictable results. All four references aim to improve the accuracy of determining spatial positions from projected optical patterns by processing image feature locations.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), and in further view of Mengchao Ma et al. (“A multidistance constraint method for three-dimensional reconstruction with coaxial fringe projection measurement system”; hereinafter “Mengchao”; copy provided by Examiner).
Regarding claims 5 and 13,
Zhao, in view of Huang and Grossberg, teach: the method of claim 1 and the depth map generating system of claim 9, including using convergence of the lines to calculate a position of the center of the projection system.
Zhao fails to explicitly disclose: wherein calculating the position of the center of the projection system using the convergence of the lines includes using a least-squares fit.
In a related art, Mengchao teaches: using multiple projector positions to generate multiple geometric constraints and solving the resulting overdetermined reconstruction equations using least constraints (see FIG.s 1 and 2; p. 2 left column lines 35-36 “the 3D data can then be calculated with the constraints for n ( n ≥ 2) positions of the projector using a least-squares algorithm”; p. 3 right column (5) “The coordinates of P ( x, y, z ) can be solved pixel-by-pixel using a least- squares method”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply Mengchao’s least-square solution to the converging lines geometry of the Zhao, Huang, and Grossberg projector-camera calibration framework because Mengchao teaches that additional projector derived constraints reduce random measurement error and improve reconstruction accuracy (see Mengcho p. 7, Conclusion section). All four references aim to improve the accuracy of determining spatial positions from projected optical patterns by processing image feature locations.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), and in further view of Semeniuta (“Analysis of camera calibration with respect to measurement accuracy”; hereinafter “Semeiuta”; copy provided by Examiner).
Regarding claims 6 and 14,
Zhao, in view of Huang and Grossberg, teach: the method of claim 1 and the depth map generating system of claim 9, including using convergence of the lines to calculate a position of the center of the projection system.
Zhao, in view of Huang and Grossberg, fail to explicitly disclose: wherein calculating the position of the center of the projection system using the convergence of the lines includes calculating the position of the center, determining outlier lines and then recalculating the position of the center without including the outlier lines.
In a related art, Semeniuta’s analysis of known camera calibration processes (Semeniuta Section 2 “Theory and Related Work”) explains: that calibration accuracy and robustness are improved by identifying and excluding outlier feature points, recalculating calibration parameters after excluding those outliers, and applying optimization techniques to improve calibration accuracy (see Semeniuta p. 767 right column lines 3-30, “accuracy and robustness of Zhang’s calibration algorithm was improved by removing outlier feature points… A feature point is considered an outlier if its projection error is unacceptably high…. The outliers are removed in two stages: (1) threshold selection, excluding the points with the largest reprojection error, and (2) RANSAC algorithm, finishing the outliers removal,” “the camera parameters are recomputed. This process is repeated until convergence,” and “minimizes 3D distance between the point of intersection of calibration plane with optical ray and known feature point”.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the calibration framework taught by the combination of Zhao, Huang, and Grossberg, to incorporate known outlier rejection techniques, exemplified by the teachings of Semeniuta, to improve the accuracy and robustness of the framework for calculating the position of the center of the projection center taught by Zhao, Huang, and Grossberg.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”; copy provided by Examiner), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), and in further view of Rowlands et al. (US 20200300977 A1; hereinafter “Rowlands”).
Regarding claim 16,
Zhao, in view of Huang and Grossberg, teach: the depth map generating system of claim 9, including a controller configured to project an array of dots.
Grossberg further teaches: calculated three-dimensional rays which extend from the imaging system outwards (Grossberg teaches calculating three-dimensional ray extending outward from the imaging system by determining, for each image point/pixel, a ray from two known plane intersections, wherein “The two points
p
n
,
p
f
determine the ray and thus the direction of the ray
q
f
” and the resulting point/direction pairs determine the discrete raxel model, representing the imaging geometry as three-dimensional rays (see p. 130 Figure 10 and p. 130 left column, lines 2-16).).
Zhao, in view of Huang and Grossberg fail to explicitly disclose: wherein the controller is further configured to determine time-of-flight for beams of light emitted from the projection system, and using calculated three-dimensional rays which extend from the imaging system outwards to allocate three-dimensional positions for dots of light based upon times of flight.
In a related art, Rowlands teaches: wherein the controller is further configured to determine time-of-flight for beams of light emitted from the projection system, and (Rowlands teaches performing time-of-flight measurements on reflected projected light to determine distances and generate three-dimensional measurements/point cloud corresponding to projected light areas or dots (¶ [0004] “perform a plurality of time-of-flight measurements on the beam.”; ¶ [0027] “determine a distance of object 150 and/or portions thereof from sensor system 105 based on the one or more time-of-flight measurements and may generate a three-dimensional measurement”; ¶ [0028] “multiple intensity dots in a dot pattern…generate a depth point cloud representing multiple distance measurements”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhao, Huang, and Grossberg to perform time-of-flight measurements and use the calculated three-dimensional rays to determine three-dimensional positions of projected dots, as taught by Rowlands, because doing so would enable depth mapping and improve the accuracy of three-dimensional position measurements of projected dots. All four references are directed towards obtaining accurate three-dimensional positions of points using projection and imaging techniques.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 20180061034 A1; hereinafter “Zhao”) in view of Huang et al. (“A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets”; hereinafter “Huang”), in further view of Grossberg & Nayar (“The Raxel Imaging Model and Ray-Based Calibration”; hereinafter “Grossberg”), in further view of Tubic et al. (US 20160350929 A1; hereinafter “Tubic”), in further view of Fan et al. (CN 101949768 B; hereinafter “Fan”), in further view of Mengchao Ma et al. (“A multidistance constraint method for three-dimensional reconstruction with coaxial fringe projection measurement system”; hereinafter “Mengchao”), in further view of Semeniuta (“Analysis of camera calibration with respect to measurement accuracy”; hereinafter “Semeiuta”), and in further view of Rowlands et al. (US 20200300977 A1; hereinafter “Rowlands”). Copies of all non-patent literature and translations provided by Examiner.
Regarding claim 8,
Zhao, Huang, Grossberg, Tubic, Fan, Mengchao, and Semeiuta teach: A method of generating a depth map comprising using a projection system and an imaging system which have been calibrated according to any preceding claim.
The method limitation(s) of claim 8 mirror the scope being performed by the controller in the depth map generating system of claim 16, taught by Zhao, Huang, Grossberg, and Rowlands. For sake of brevity, refer to claim 16’s 103 rejection above for corresponding teachings. Accordingly, claim 8 is reject based on the prior art teachings as seen above and the motivation to combine identified with respect to claim 16 is also applicable for claim 8.
Relevant Art Not Relied Upon
The following prior art is made of record and not relied upon, but is considered pertinent to applicant’s disclosure:
Sugimoto (“Directionally Decomposing Structured Light for Projector Calibration”): teaches a projector calibration technique and device that directionally decomposes structured light using a pinhole array mask to extract projector rays and accurately estimate the projector’s intrinsic parameters by treating the projector as a pinhole camera.
Conclusion
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/SDB/
Samuel D. Baynes
Examiner | Art Unit 2665
/BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665
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