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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 2 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments, by Vizzo et al.
With respect to claim 1, Vizzo discloses A method of forming a three-dimensional (3D) opacity grid comprising (see Abstract): mapping light detection and ranging (LiDAR) points to a grid; employing a volume densification to the grid to generate a 3D opacity grid representing a surrounding scene, (see figure 2, and page 8536, left hand column), as claimed.
With respect to claim 2, Vizzo further discloses wherein mapping the LiDAR points to a grid comprises: mapping each LiDAR point to a voxel grid; and setting a voxel value to a constant σ0 to initialize a sparse grid of spatial occupancy, (see page 8536 last paragraph wherein …To obtain the TSDF representation of the scan, we employ VDBFusion [36] with voxel size of Vsize…), as claimed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-8, 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments, by Vizzo et al. in view of Learning to Generate Realistic LiDAR Point Clouds, by Zyrianov et al.
With respect to claim 3, Vizzo discloses all the elements al claimed and rejected in claim 1 above. However, Vizzo fails to explicitly disclose wherein employing a volume densification comprises filling the LiDAR points having sparse spatial occupancy with a low-dimensional representation space, as claimed.
Zyrianov teaches employing a volume densification comprises filling the LiDAR points having sparse spatial occupancy with a low-dimensional representation space, (see page 24 first paragraph wherein …Our input representation enjoys several benefits. Firstly, it encodes information into a dense and compact 2D map, allowing us to exploit efficient network architecture transferred from the 2D image generation domain…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of 3D opacity of surrounding. Teaching of Zyrianov to have low dimensional representation of the space can be incorporated into the Vizzo system as suggested (see figure 2), and modifying the system yields a representation of the surrounding scene (see figure 2 of Zyrianov), for motivation.
With respect to claim 4, combination of Vizzo and Zyrianov further discloses wherein employing a volume densification comprises: using an autoencoder to map the LiDAR points having sparse spatial occupancy to a low dimensional manifold; and decoding the low dimensional manifold to reconstruct the 3D opacity grid representing the surrounding scene, (see Zyrianov figure 2 and Vizzo figure 2 Make it Dense), as claimed.
With respect to claim 5, combination of Vizzo and Zyrianov further discloses wherein employing a volume densification comprises: using an encoder to map the initialized sparse grid of spatial occupancy into a low dimensional feature vector with a series of convolution layers; and using a decoder to up sample intermediate features with convolution to reconstruct the sparse grid of spatial occupancy to a same size as inputted, (see Vizzo page 8536 right hand column last two lines to page 8537 left hand column first three paragraphs), as claimed.
With respect to claim 6, combination of Vizzo and Zyrianov further discloses extracting low-frequency information from the initialized sparse grid of spatial occupancy, (see Vizzo page 8537, right hand column last paragraph before sub section C., wherein … Nevertheless, our network can learn how to complete a sparse input scan in a coarse-to-fine fashion with a high level of detail and completeness), as claimed.
With respect to claim 7, combination of Vizzo and Zyrianov further discloses removing skip connections between the encoder and decoder layers so only low-frequency signal are passed through, (see Vizzo page 8536 right hand column last two lines to page 8537 left hand column first three lines; and Zyrianov page 28, first line), as claimed.
With respect to claim 8, combination of Vizzo and Zyrianov further discloses randomly rotating and translating the LiDAR points, (see Vizzo page 8536 left hand column section A. Scan Integration Using TSDF, wherein … the rotational part and the translational part of the transformation…), as claimed.
Claim 12 is rejected for the same reasons as set forth in the rejections of claim 3, because claim 12 is claiming subject matter similar to claim 3.
Claims 13-18 are rejected for the same reasons as set forth in the rejections of claim 2, 4, 5, 6, 7 and 8, because claims 13-18 are claiming subject matter similar to claims 2, 4, 5, 6, 7 and 8 respectively.
Claim 20 is rejected for the same reasons as set forth in the rejections of limitations in claims 1+2+3+5+7, because claim 20 is claiming subject matter similar as combination of limitations in claims 1+2+3+5+7.
Claims 9-11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments, by Vizzo et al. in view of AlieV et al (WO 2021/096190).
With respect to claim 9, Vizzo discloses all the elements as claimed and rejected in claim 1 above. However, Vizzo fails to explicitly disclose using a forecasting network to take historical 3D opacity grids as input to predict future 3D opacity grids, as claimed.
Aliev teaches using a forecasting network to take historical 3D opacity grids as input to predict future 3D opacity grids, (see page 3, lines 26-30, wherein … neural rendering implies learning arbitrary scene representation in order to generate realistic imagery and manipulate its appearance (from scene attributes manipulation to inpainting). For instance, Neural Volumes (16) is based on prediction of 4D volume (RGB + opacity) for a model based on several photos by variational autoencoder, warping the volume and its opacity-aware integration…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of 3D opacity of surrounding. Teaching of Aliev to have neural network or opacity measurement can be incorporated into the Vizzo system as suggested (see figure 2), and modifying the system yields a representation of the surrounding scene (see figure 1 of Aliev), for motivation.
With respect to claim 10, combination of Vizzo and Aliev further discloses wherein the forecasting network transforms each LiDAR point from a local sensor coordinate to a coordinate at frame t based on LiDAR pose in each frame, (see Aliev figure 2 for the entire architecture of the system).
With respect to claim 11, combination of Vizzo and Aliev further discloses wherein the forecasting network is a UNET-style 3D convolutional encoder-decoder network, wherein each pair of corresponding layers in the encoder-decoder network with the same feature size is connected by a skip layer, (see Aliev figure 2 U-NET), as claimed.
Claim 19 is rejected for the same reasons as set forth in the rejections of claim 9, because claim 19 is claiming subject matter similar to claim 9.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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