关键词: 点云, 点云补全, 多模态, 自监督, 交叉注意力, 几何感知

Abstract: To address the issue of incomplete point clouds caused by occlusion, concavities, illumination, and other factors during object scanning with LiDAR devices, this study proposes a 2D view-guided 3D point cloud completion method. Existing view-guided approaches typically fuse 2D and 3D information either explicitly or implicitly. Considering that the fused features should simultaneously retain global information from 2D data and local details for point cloud refinement, we design a novel network that integrates 2D views with point clouds for completion. The network operates in two stages: feature extraction/encoding and feature decoding/point generation. In the first stage, DGCNN and ResNet are employed to extract features from point clouds and 2D views, respectively. These features are then fused via an Attention mechanism to generate hybrid representations, followed by downsampling to obtain global features. In the second stage, the fused features are decoded using Attention mechanisms and upsampled via transposed convolution to reconstruct complete point clouds. Experimental results demonstrate that our method achieves superior Chamfer Distance (CD) metrics on the ShapeNet-ViPC dataset compared to state-of-the-art single-modal and multi-modal point cloud completion approaches.

Key words: point cloud, point cloud completion, multi-modelity, self-supervised, Cross-Attention, geometry-aware