公开(公告)号：US20210166052A1

公开(公告)日：2021-06-03

申请号：US17109421

申请日：2020-12-02

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Yilin Yang ,  Xiaolin Lin ,  Abhishek Bajpayee ,  Hae-Jong Seo ,  Eric Jonathan Yuan ,  Xudong Chen

IPC: G06K9/46 ,  G05D1/00 ,  G06K9/62 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

公开(公告)号：US20200026960A1

公开(公告)日：2020-01-23

申请号：US16514230

申请日：2019-07-17

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G06K9/62 ,  G06N3/04 ,  G05D1/00 ,  G06K9/00

Abstract: In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.