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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| Published in: | IEEE signal processing letters Vol. 32; pp. 4444 - 4448 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1070-9908, 1558-2361 |
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| Abstract | 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. Multi-head self-attention captures robust contextual relationships, while residual connections in the autoencoder enhance geometric feature learning. In addition to this, we incorporate a contrastive learning-based loss, which encourages the network to better distinguish structural patterns even in highly incomplete observations. Experimental results on benchmark datasets demonstrate that the proposed approach achieves state-of-the-art performance in single-view point cloud completion. |
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| AbstractList | 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. Multi-head self-attention captures robust contextual relationships, while residual connections in the autoencoder enhance geometric feature learning. In addition to this, we incorporate a contrastive learning-based loss, which encourages the network to better distinguish structural patterns even in highly incomplete observations. Experimental results on benchmark datasets demonstrate that the proposed approach achieves state-of-the-art performance in single-view point cloud completion. |
| Author | Kumar, Preyum Kumari, Seema Raman, Shanmuganathan Mandal, Srimanta |
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| Snippet | Point cloud completion aims to reconstruct complete 3D shapes from partial observations, often requiring multiple views or complete data for training. In this... |
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| SubjectTerms | 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 |
| Title | Contrastive Attention-Based Network for Self-Supervised Point Cloud Completion |
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