1University of Sanya (China) 2China University of Geosciences (China) 3Geely Automobile Research Institute (Ningbo) Co., Ltd. (China) 4Ordos Vocational College (China)
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Recovering a complete point cloud from a partial point cloud is a critical and challenging task for many 3D applications. In this paper, a point cloud completion network is proposed that focuses on improving the point cloud feature extraction and the initial generated point cloud in the encoding phase. The local details of the original point cloud are highlighted by introducing trigonometric positional embedding for point cloud encoding. Moreover, a self-attention mechanism for feature fusion is proposed to facilitate the generation of a complete point cloud. The experiments on multiple public datasets demonstrate that our network effectively achieves 3D point cloud completion with strong generalization, outperforming recent point cloud completion methods.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yidong Yang,Weijun Peng,Boxiong Yang,Huachao Wu,Yiqi Wu,Yong Chen, andYanli Li
"Point cloud completion via trigonometric encoding and self-attention-based feature fusion", Proc. SPIE 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 135391D (13 February 2025); https://doi.org/10.1117/12.3057669
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