公开(公告)号：US20240249538A1

公开(公告)日：2024-07-25

申请号：US18223473

申请日：2023-07-18

Applicant: NVIDIA Corporation

Inventor： Zetong Yang ,  Zhiding Yu ,  Ren Hao Wang ,  Chris Choy ,  Anima Anandkumar ,  Jose M. Alvarez Lopez

IPC: G06V20/64 ,  G06T7/50 ,  G06T7/70 ,  G06T7/80 ,  G06V10/22 ,  G06V10/82

CPC classification number: G06V20/64 ,  G06T7/50 ,  G06T7/70 ,  G06T7/80 ,  G06V10/225 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30252 ,  G06V2201/07

Abstract: 3D object detection is a computer vision task that generally detects (e.g. classifies and localizes) objects in 3D space from the 2D images or videos that capture the objects. Current techniques used for 3D object detection rely on machine learning processes that learn to detect 3D objects from existing images annotated with high-quality 3D information including depth information generally obtained using lidar technology. However, due to lidar's limited measurable range, current machine learning solutions to 3D object detection do not support detection of 3D objects beyond the lidar range, which is needed for numerous applications, including autonomous driving applications where existing close or midrange 3D object detection does not always meet the safety-critical requirement of autonomous driving. The present disclosure provides for 3D object detection using a technique that supports long-range detection (i.e. detection beyond the lidar range).

公开(公告)号：US20240119291A1

公开(公告)日：2024-04-11

申请号：US18203552

申请日：2023-05-30

Applicant: NVIDIA Corporation

Inventor： Jose M. Alvarez Lopez ,  Pavlo Molchanov ,  Hongxu Yin ,  Maying Shen ,  Lei Mao ,  Xinglong Sun

IPC: G06N3/082 ,  G06N3/0495

CPC classification number: G06N3/082 ,  G06N3/0495

Abstract: Machine learning is a process that learns a neural network model from a given dataset, where the model can then be used to make a prediction about new data. In order to reduce the size, computation, and latency of a neural network model, a compression technique can be employed which includes model sparsification. To avoid the negative consequences of pruning a fully pretrained neural network model and on the other hand of training a sparse model in the first place without any recovery option, the present disclosure provides a dynamic neural network model sparsification process which allows for recovery of previously pruned parts to improve the quality of the sparse neural network model.

公开(公告)号：US20240221166A1

公开(公告)日：2024-07-04

申请号：US18395198

申请日：2023-12-22

Applicant: NVIDIA Corporation

Inventor： Zhiding Yu ,  Shuaiyi Huang ,  De-An Huang ,  Shiyi Lan ,  Subhashree Radhakrishnan ,  Jose M. Alvarez Lopez ,  Anima Anandkumar

IPC: G06T7/12 ,  G06V10/764 ,  G06V20/70

CPC classification number: G06T7/12 ,  G06V10/764 ,  G06V20/70 ,  G06T2207/20081

Abstract: Video instance segmentation is a computer vision task that aims to detect, segment, and track objects continuously in videos. It can be used in numerous real-world applications, such as video editing, three-dimensional (3D) reconstruction, 3D navigation (e.g. for autonomous driving and/or robotics), and view point estimation. However, current machine learning-based processes employed for video instance segmentation are lacking, particularly because the densely annotated videos needed for supervised training of high-quality models are not readily available and are not easily generated. To address the issues in the prior art, the present disclosure provides point-level supervision for video instance segmentation in a manner that allows the resulting machine learning model to handle any object category.