Scene flow estimation is a crucial component in the development of autonomous driving and 3D robotics, providing valuable information for environment perception and navigation. Despite the advantages of learning-based scene flow estimation techniques, their domain specificity and limited generalizability across varied scenarios pose challenges. In contrast, non-learning optimization-based methods, incorporating robust priors or regularization, offer competitive scene flow estimation performance, require no training, and show extensive applicability across datasets, but suffer from lengthy inference times. In this paper, we present OptFlow, a fast optimization-based scene flow estimation method. Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks. It integrates a local correlation weight matrix for correspondence matching, an adaptive correspondence threshold limit for nearest-neighbor search, and graph prior rigidity constraints, resulting in expedited convergence and improved point correspondence identification. Moreover, we demonstrate how integrating a point cloud registration function within our objective function bolsters accuracy and differentiates between static and dynamic points without relying on external odometry data. Consequently, OptFlow outperforms the baseline graph-prior method by approximately 20% and the Neural Scene Flow Prior method by 5%-7% in accuracy, all while offering the fastest inference time among all non-learning scene flow estimation methods.

翻译：[translated abstract in Chinese] 场景流估计是自动驾驶与三维机器人技术发展的关键组成部分，为环境感知与导航提供重要信息。尽管基于学习的场景流估计技术具有优势，但其领域特异性与跨场景泛化能力有限的问题仍构成挑战。相比之下，融合鲁棒先验或正则化的非学习优化方法，虽无需训练且能提供具有竞争力的场景流估计性能，并在不同数据集中展现广泛适用性，但存在推理时间过长的缺陷。本文提出OptFlow——一种基于优化的快速场景流估计方法。该方法无需依赖学习或任何标注数据集，即在主流自动驾驶基准测试中达到场景流估计的先进性能。通过集成局部相关权重矩阵进行对应匹配、自适应对应阈值限制优化最近邻搜索、以及图先验刚性约束，OptFlow实现了更快的收敛速度与更优的点对应识别效果。此外，我们证明在目标函数中融入点云配准模块，可在不依赖外部里程计数据的情况下提升精度，并区分静态与动态点。最终，OptFlow在精度上较基线图先验方法提升约20%，较神经场景流先验方法提升5%-7%，同时所有非学习场景流估计方法中推理速度最快。