Enhancing Point Cloud Completion with Fine-Grained Geometric Perception

Point clouds are crucial for representing 3D objects, yet they often suffer from incompleteness due to various factors. Traditional methods for point cloud completion focus on global shape integrity but neglect fine-grained geometric features. This study introduces an Enhancing Point Cloud Completion with Fine-Grained Geometric Perception (FGGP-PCC) model that operates across multiple res-olutions. By integrating a Joint Local Multi-Layer Perceptron (JL-MLP) with an attention mechanism, FGGP-PCC effectively captures both local detailsand long-range semantic dependencies. Our approach significantly enhances themodel’s ability to reason about complex geometric complementarity. Experimental results on benchmark datasets such as ShapeNet-55, PCN, and Completion3D demonstrate state-of-the-art performance, with FGGP-PCC achieving an average reduction of 3.45% in Chamfer Distance across different completion levels. The source code is available at https://github.com/ChinaZlm2022/Loss-Edge-Merging-with-Attention.