公开(公告)号：US20240265619A1

公开(公告)日：2024-08-08

申请号：US18509428

申请日：2023-11-15

Applicant: NVIDIA Corporation

Inventor： Faycal Ait Aoudia ,  Jakob Richard Hoydis ,  Nikolaus Binder ,  Merlin Nimier-David ,  Sebastian Cammerer ,  Alexander Georg Keller ,  Guillermo Anibal Marcus Martinez

IPC: G06T15/06 ,  G06F30/27 ,  G06N3/04

CPC classification number: G06T15/06 ,  G06F30/27 ,  G06N3/04

Abstract: Embodiments of the present disclosure relate to learning digital twins of radio environments. Differentiable ray tracing may be used to refine the scene geometry of the physical environment, to learn or optimize the scene properties of objects in the scene, to learn or optimize the scene properties of antennas, and to learn or optimize antenna patterns, array geometries, and orientations and positions of transmitters and receivers. Once scene properties have been learned or optimized, the differentiable ray tracer may further be used to simulate radio wave propagation to simulate the performance of different configurations of the scene geometry and radio devices, such as antennas. In an embodiment, one or more of the scene geometry, scene properties, and antenna characteristics are computed by a differentiable parametric function, such as a neural network, etc. and parameters of the differentiable parametric function are learned using the differentiable ray tracing.

公开(公告)号：US20250104329A1

公开(公告)日：2025-03-27

申请号：US18653088

申请日：2024-05-02

Applicant: NVIDIA Corporation

Inventor： Jakob Richard Hoydis ,  Faycal Ait Aoudia ,  Sebastian Cammerer ,  Alexander Georg Keller ,  Merlin Nimier-David ,  Nikolaus Binder ,  Guillermo Anibal Marcus Martinez

IPC: G06T15/06

Abstract: Embodiments of the present disclosure relate to neural components for differentiable ray tracing of radio propagation. Differentiable ray tracing may be used to refine the scene geometry of the physical environment, to learn or optimize the scene properties of objects in the scene, to learn or optimize the scene properties of antennas, and to learn or optimize antenna patterns, array geometries, and orientations and positions of transmitters and receivers. Once scene properties have been learned or optimized, the differentiable ray tracer may further be used to simulate the performance of different configurations of the transmitters, receivers, and scene geometry. In an embodiment, one or more of the scene geometry, scene properties, and antenna characteristics are computed by a differentiable parametric function, such as a neural network, etc. and parameters of the differentiable parametric function are learned using the differentiable ray tracing.

公开(公告)号：US20250095275A1

公开(公告)日：2025-03-20

申请号：US18630480

申请日：2024-04-09

Applicant: NVIDIA Corporation

Inventor： Zian Wang ,  Tianchang Shen ,  Jun Gao ,  Merlin Nimier-David ,  Thomas Müller-Höhne ,  Alexander Keller ,  Sanja Fidler ,  Zan Gojcic ,  Nicholas Mark Worth Sharp

IPC: G06T15/06 ,  G06V10/44

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.