Contrastive Attention-Based Network for Self-Supervised Point Cloud Completion

Point cloud completion aims to reconstruct complete 3D shapes from partial observations, often requiring multiple views or complete data for training. In this paper, we propose an attention-driven, self-supervised autoencoder network that completes 3D point clouds from a single partial observation....

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Vydané v:	IEEE signal processing letters Ročník 32; s. 4444 - 4448
Hlavní autori:	Kumari, Seema, Kumar, Preyum, Mandal, Srimanta, Raman, Shanmuganathan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	and self-supervision Auto-encoder Autoencoders contrastive learning Decoding Image reconstruction multi-head self-attention Noise point cloud completion Point cloud compression Semantics Shape Surface reconstruction Three dimensional models Three-dimensional displays Training Transformers
ISSN:	1070-9908, 1558-2361
On-line prístup:	Získať plný text
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