VFC-Net: Point Cloud Completion via Voxel-Based Transformer

Point cloud completion aims to recover missing regions in 3D point clouds caused by sensor limitations and environmental occlusions. Although recent methods have achieved remarkable progress in recovering the overall shape of incomplete objects, explicitly controlling the spatial distribution of reconstructed points remains challenging, often leading to outliers and non-uniform point distributions. In this paper, we propose a novel framework, VFC-Net, which generates a uniformly distributed coarse point cloud to effectively guide dense reconstruction. Specifically, VFC-Net adopts a generation-upsampling paradigm. In the generation stage, termed VoxGen, the input point cloud is first converted into a voxel-based representation, from which a coarse yet complete point cloud is generated via voxel occupancy classification, naturally encouraging a uniform spatial distribution. In the upsampling stage, termed VoxPu, voxel occupancy predictions are fused with voxel features to guide dense reconstruction, thereby preserving the distribution consistency of the final output. Furthermore, we introduce a lightweight VoxAttn module in both stages to efficiently capture missing geometric structures. By decomposing the voxel grid into a set of 2D slices and performing attention-based feature aggregation, VoxAttn significantly reduces the computational complexity. To compensate for the information loss introduced by slicing, we further employ alternating slicing axes together with cross-slice feature aggregation. Extensive experiments on multiple challenging benchmarks demonstrate that VFC-Net achieves state-of-the-art performance in point cloud completion.