公开(公告)号：US20220261593A1

公开(公告)日：2022-08-18

申请号：US17177068

申请日：2021-02-16

Applicant: NVIDIA Corporation

Inventor： Zhiding Yu ,  Shiyi Lan ,  Chris Choy ,  Subhashree Radhakrishnan ,  Guilin Liu ,  Yuke Zhu ,  Anima Anandkumar

IPC: G06K9/62 ,  G06T7/11 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained to perform segmentation tasks based at least in part on training data comprising bounding box annotations.

公开(公告)号：US20220126445A1

公开(公告)日：2022-04-28

申请号：US17082972

申请日：2020-10-28

Applicant: NVIDIA Corporation

Inventor： Yuke Zhu ,  Yifeng Zhu ,  Stanley Thomas Birchfield ,  Jonathan Tremblay

IPC: B25J9/16

Abstract: Apparatuses, systems, and techniques are described that solve task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.

公开(公告)号：US20240257443A1

公开(公告)日：2024-08-01

申请号：US18524803

申请日：2023-11-30

Applicant: NVIDIA Corporation

Inventor： Christopher B. Choy ,  Or Litany ,  Charles Loop ,  Yuke Zhu ,  Animashree Anandkumar ,  Wei Dong

IPC: G06T15/20 ,  G06T1/20 ,  G06T5/50 ,  G06T5/70 ,  G06T7/579 ,  G06T7/90 ,  G06T19/20

CPC classification number: G06T15/20 ,  G06T1/20 ,  G06T5/50 ,  G06T5/70 ,  G06T7/579 ,  G06T7/90 ,  G06T19/20 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2210/04 ,  G06T2210/21 ,  G06T2219/2012

Abstract: A technique for reconstructing a three-dimensional scene from monocular video adaptively allocates an explicit sparse-dense voxel grid with dense voxel blocks around surfaces in the scene and sparse voxel blocks further from the surfaces. In contrast to conventional systems, the two-level voxel grid can be efficiently queried and sampled. In an embodiment, the scene surface geometry is represented as a signed distance field (SDF). Representation of the scene surface geometry can be extended to multi-modal data such as semantic labels and color. Because properties stored in the sparse-dense voxel grid structure are differentiable, the scene surface geometry can be optimized via differentiable volume rendering.