US 12,008,822 B2
Using neural networks for 3D surface structure estimation based on real-world data for autonomous systems and applications
Kang Wang, Bellevue, WA (US); Yue Wu, Mountain View, CA (US); Minwoo Park, Saratoga, CA (US); and Gang Pan, Fremont, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Oct. 28, 2021, as Appl. No. 17/452,752.
Prior Publication US 2023/0135234 A1, May 4, 2023
Int. Cl. G06V 20/64 (2022.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G06F 18/214 (2023.01); G06V 20/58 (2022.01); B60G 17/0165 (2006.01); B60K 31/00 (2006.01); B60W 60/00 (2020.01)
CPC G06V 20/64 (2022.01) [G01S 17/89 (2013.01); G01S 17/931 (2020.01); G06F 18/214 (2023.01); G06V 20/58 (2022.01); B60G 17/0165 (2013.01); B60K 31/00 (2013.01); B60W 60/001 (2020.02); B60W 2420/408 (2024.01)] 21 Claims
OG exemplary drawing
 
1. A method comprising:
generating, based at least on image data captured during a capture session in an environment, a first representation of a three-dimensional (3D) surface structure of a component of the environment;
generating, based at least on LiDAR data associated with the image data and captured during the capture session, a second representation of the 3D surface structure of the component; and
training one or more neural networks (NNs) to generate a densified representation of the 3D surface Lure using the first representation of the 3D surface structure as input training data and using the second representation of the 3D surface structure as around truth training data.