公开(公告)号：US20230360372A1

公开(公告)日：2023-11-09

申请号：US18223575

申请日：2023-07-19

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Fuqiang ZHAO ,  Minye WU ,  Lan XU ,  Jingyi YU

IPC: G06V10/774 ,  G06V10/82 ,  G06T7/50 ,  G06T7/80 ,  G06V10/74 ,  G06V20/64

CPC classification number: G06V10/774 ,  G06V10/82 ,  G06T7/50 ,  G06T7/80 ,  G06V10/761 ,  G06V20/64 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/10028 ,  G06V40/172

Abstract: Systems, methods, and non-transitory computer-readable media are configured to obtain a set of content items to train a neural radiance field-based (NeRF-based) machine learning model for object recognition. Depth maps of objects depicted in the set of content items can be determined. A first set of training data comprising reconstructed content items depicting only the objects can be generated based on the depth maps. A second set of training data comprising one or more optimal training paths associated with the set of content items can be generated based on the depth maps. The one or more optimal training paths are generated based at least in part on a dissimilarity matrix associated with the set of content items. The NeRF-based machine learning model can be trained based on the first set of training data and the second set of training data.