关键词: 点云补全, 局部特征, 自适应邻域, 局部感知, 交叉注意力

Abstract: Point cloud completion aims to reconstruct a high-quality complete point cloud from incomplete point cloud data. However, most existing point cloud completion networks have limitations in capturing local features and reconstructing details, resulting in poor performance in terms of local details and completion accuracy. To address these issues, a multi-stage point cloud completion Network based on Adaptive Neighborhood Feature Fusion (ANFF-Net) was proposed. Firstly, the neighborhood selection of key points was adjusted by the feature extractor adaptively to adapt to different shapes of point clouds, effectively capturing the spatial relationships between points with different semantics, thereby reducing the loss of local details. Then, a local perception Transformer was used by the feature expander to further expand the local feature information of neighboring points, improving the network's ability to recover details. Finally, a cross-attention mechanism was applied by the point cloud generator to selectively propagate the local feature information of the incomplete point cloud and a folding module was used to gradually refine the local regions, significantly enhancing the detail retention of the completed point cloud and generating more consistent geometric details.  Experimental results show that ANFF-Net improves the completion accuracy by 9.67 percentage points compared to ProxyFormer on the ShapeNet55 dataset and achieves good completion performance on the PCN and KITTI datasets. The visualization results indicate that the point clouds generated by ANFF-Net have finer granularity and are closer in shape to the ground truth.

Key words: point cloud completion, local feature, adaptive neighborhood, local perception, cross-attention