PonderV2: Improved 3D Representation With a Universal Pre-Training Paradigm

In contrast to numerous NLP and 2D vision foundational models, training a 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data variability and diversity of downstream tasks. In this paper, we introduce a novel universal 3D pre-training framework des...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 47; číslo 8; s. 6550 - 6565
Hlavní autori:	Zhu, Haoyi, Yang, Honghui, Wu, Xiaoyang, Huang, Di, Zhang, Sha, He, Xianglong, Zhao, Hengshuang, Shen, Chunhua, Qiao, Yu, He, Tong, Ouyang, Wanli
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2025
Predmet:	3D pre-training 3D vision Autoencoders Benchmark testing foundation model Geometry Image reconstruction LiDAR multi-view image Neural radiance field neural rendering point cloud Point cloud compression Rendering (computer graphics) RGB-D image Solid modeling Three-dimensional displays Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
On-line prístup:	Získať plný text
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