DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement
Authors: Jiuming Liu, Guangming Wang, Weicai Ye, Chaokang Jiang, Jinru Han, Zhe Liu, Guofeng Zhang, Dalong Du, Hesheng Wang

Scene flow estimation which aims to predict per-point 3D displacements of dynamic scenes is a fundamental task in the computer vision field. However previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems we propose a novel uncertainty-aware scene flow estimation network (DifFlow3D) with the diffusion probabilistic model. Iterative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases e.g. dynamics noisy inputs repetitive patterns etc. To restrain the generation diversity three key flow-related features are leveraged as conditions in our diffusion model. Furthermore we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow. Our DifFlow3D achieves state-of-the-art performance with 24.0% and 29.1% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably our method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D) on the KITTI dataset. Additionally our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into existing scene flow networks significantly increasing their estimation accuracy. Codes are released at https://github.com/IRMVLab/DifFlow3D.