Kumari, SeemaSeemaKumariKumar, PreyumPreyumKumarMandal, SrimantaSrimantaMandalRaman, ShanmuganathanShanmuganathanRaman2025-11-262025-11-262025-01-0110.1109/LSP.2025.36314242-s2.0-105021537819http://repository.iitgn.ac.in/handle/IITG2025/33545Point 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.falseand Self-Supervision | Auto-encoder | Contrastive Learning | Multi-Head Self-Attention | Point Cloud CompletionArticle1558236120250