DFG-PCN: Point Cloud Completion with Degree-Flexible Point Graph

• URL: http://arxiv.org/abs/2509.23703v1
• Date: Sun, 28 Sep 2025 07:28:42 GMT
• Title: DFG-PCN: Point Cloud Completion with Degree-Flexible Point Graph
• Authors: Zhenyu Shu, Jian Yao, Shiqing Xin,
• Abstract summary: This paper proposes a point cloud completion framework called Degree-Flexible Point Graph Completion Network (DFG-PCN)<n>It adaptively assigns node degrees using a detail-aware metric that combines feature variation and curvature, focusing on structurally important regions.
• Score: 17.079595595415466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud completion is a vital task focused on reconstructing complete point clouds and addressing the incompleteness caused by occlusion and limited sensor resolution. Traditional methods relying on fixed local region partitioning, such as k-nearest neighbors, which fail to account for the highly uneven distribution of geometric complexity across different regions of a shape. This limitation leads to inefficient representation and suboptimal reconstruction, especially in areas with fine-grained details or structural discontinuities. This paper proposes a point cloud completion framework called Degree-Flexible Point Graph Completion Network (DFG-PCN). It adaptively assigns node degrees using a detail-aware metric that combines feature variation and curvature, focusing on structurally important regions. We further introduce a geometry-aware graph integration module that uses Manhattan distance for edge aggregation and detail-guided fusion of local and global features to enhance representation. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art approaches.

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