公开(公告)号：US20240428514A1

公开(公告)日：2024-12-26

申请号：US18415454

申请日：2024-01-17

Applicant: NVIDIA Corporation

Inventor： Kang WANG ,  Yue WU ,  Minwoo PARK ,  Gang PAN

IPC: G06T17/05 ,  B60W30/09 ,  B60W30/14 ,  B60W40/06 ,  B60W50/06 ,  B60W60/00 ,  G06F18/214 ,  G06V20/05 ,  G06V20/58

Abstract: 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.

公开(公告)号：US20230351769A1

公开(公告)日：2023-11-02

申请号：US17733508

申请日：2022-04-29

Applicant: NVIDIA Corporation

Inventor： Yue WU ,  Liwen Lin ,  Xin Tong ,  Gang Pan

IPC: G06V20/58 ,  G06T7/55 ,  G06V10/74 ,  G06V10/764 ,  G06V10/762

CPC classification number: G06V20/58 ,  G06T7/55 ,  G06V10/761 ,  G06V10/764 ,  G06V10/762 ,  G06T2207/20081 ,  G06T2207/30261 ,  B60W60/0015

Abstract: In various examples, systems and methods for machine learning based hazard detection for autonomous machine applications using stereo disparity are presented. Disparity between a stereo pair of images is used to generate a path disparity model. Using the path disparity model, a machine learning model can recognize when a pixel in the first image corresponds to a pixel in the second image even though the pixel in the two images does not have identical characteristics. Similarities in extracted feature vectors can be computed and represented by a vector similarity metric that is input to a machine learning classifier, along with feature information extracted from the stereo image pair, to differentiate hazard pixels from non-hazard pixels. In some embodiments, a V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.

公开(公告)号：US20230351638A1

公开(公告)日：2023-11-02

申请号：US17733497

申请日：2022-04-29

Applicant: NVIDIA Corporation

Inventor： Yue WU ,  Liwen Lin ,  Cheng-Chieh Yang ,  Gang Pan

IPC: G06T7/00 ,  G06V20/56 ,  G06V10/762 ,  G06V10/764 ,  G06V10/75 ,  G06V10/25

CPC classification number: G06T7/97 ,  G06V20/56 ,  G06V10/762 ,  G06V10/764 ,  G06V10/751 ,  G06V10/25 ,  G06T2207/10012 ,  G06T2207/20021 ,  G06T2207/20228 ,  G06T2207/30252

Abstract: In various examples, system and methods for stereo disparity based hazard detection for autonomous machine applications are presented. Example embodiments may assist an ego-machine in detecting hazards within its path of travel. The systems and methods may use disparity between a stereo pair of images to generate a baseline path disparity model and further identify hazards from detected disparities that deviate from that path disparity model. A disparity map for the image pair is constructed in which each pixel represents a disparity for a corresponding element of the image captured. Blockwise division may be optionally used to subdivide the disparity map into a plurality of smaller disparity maps, each corresponding to a block of pixels of the disparity map. A V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.