Dense structure distillation towards recurrent scene flow estimation for sparse point cloud

Abstract Scene flow, a key representation of motion information in 3D space, plays a critical role in numerous downstream tasks. However, existing point cloud scene flow estimation methods often experience significant performance degradation due to insufficient feature expressiveness or mismatching under sparse point cloud conditions. To alleviate these problems, we propose a novel dense structure distillation towards recurrent scene flow estimation method for sparse point clouds, significantly enhancing performance under low-density point cloud conditions through a dense structure distillation strategy. This module addresses the information loss caused by point cloud sparsity by leveraging high-quality point cloud features to effectively guide the learning of sparse point cloud feature extractors. To further refine the estimation results, a recurrent update strategy is adopted to gradually improve the accuracy and stability of scene flow estimation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on both the public FlyingThings3D and KITTI datasets, particularly under sparse point cloud conditions, and outperforms existing methods.