公开(公告)号：US20250111476A1

公开(公告)日：2025-04-03

申请号：US18890544

申请日：2024-09-19

Applicant: NVIDIA Corporation

Inventor： Benjamin David Eckart ,  Anthea Li ,  Chao Liu ,  Kevin Shih ,  Jan Kautz

IPC: G06T3/4046

Abstract: Parametric distributions of data are one type of data model that can be used for various purposes such as for computer vision tasks that may include classification, segmentation, 3D reconstruction, etc. These parametric distributions of data may be computed from a given data set, which may be unstructured and/or which may include low-dimensional data. Current solutions for learning parametric distributions of data involve explicitly learning kernel parameters. However, this explicit learning approach is not only inefficient in that it requires a high computational cost (i.e. from a large number of floating point operations per second), but it also leaves room for improvement in terms of accuracy of the resulting learned model. The present disclosure provides a neural network architecture that implicitly learns a parametric distribution of data, which can reduce the computational cost while improve accuracy when compared with prior solutions that rely on the explicit learning design.