LiFlow: Flow Matching for 3D LiDAR Scene Completion

In autonomous driving scenarios, the collected LiDAR pointclouds can be affected by occlusion and long-range sparsity, limitingthe perception of autonomous driving systems. Scene completion meth-ods can infer the missing parts of incomplete single LiDAR scans. Re-cent methods adopt point-level denoising diffusion probabilistic modelsthat rely on approximations to handle scene-scale data, leading to amismatch between training and inference initial distributions. We pro-pose LiFlow, the first point-level flow matching method for 3D LiDARscene completion. LiFlow improves upon existing diffusion-based meth-ods by directly aligning single LiDAR scans to complete scenes, en-suring consistent initial distributions between training and inference.LiFlow introduces nearest neighbor flow matching and Chamfer distancematching to enhance both local structure and global coverage in thealignment of point clouds. Compared to existing diffusion-based meth-ods, LiFlow reduces the number of inference steps, enabling more ef-ficient scene completion while maintaining stability and high genera-tion quality. Extensive experiments demonstrate that LiFlow achievesstate-of-the-art performance across multiple metrics. Code is availableat https://github.com/matteandre/LiFlow.