Nvidia corporation (20240428514). 3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Contents
3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Yue Wu of Mountain View CA (US)
Minwoo Park of Saratoga CA (US)
3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
This abstract first appeared for US patent application 20240428514 titled '3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Original Abstract Submitted
in various examples, to support training a deep neural network (dnn) to predict a dense representation of a 3d surface structure of interest, a training dataset is generated using a parametric mathematical modeling. a variety of synthetic 3d road surfaces may be generated by modeling a 3d road surface using varied parameters to simulate changes in road direction and lateral surface slope. in an example embodiment, a synthetic 3d road surface may be created by modeling a longitudinal 3d curve and expanding the longitudinal 3d curve to a 3d surface, and the resulting synthetic 3d surface may be sampled to form a synthetic ground truth projection image (e.g., a 2d height map). to generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3d structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.
