3D scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3D scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering methods, which cannot characterize local regions with similar motions for complex 3D scenes well, leading to inaccurate scene flow estimation. To this end, we propose an iterative end-to-end superpoint based scene flow estimation framework, where the superpoints can be dynamically updated to guide the point-level flow prediction. Specifically, our framework consists of a flow guided superpoint generation module and a superpoint guided flow refinement module. In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds. With the generated superpoints, we first reconstruct the flow for each point by adaptively aggregating the superpoint-level flow, and then encode the consistency between the reconstructed flow of pairwise point clouds. Finally, we feed the consistency encoding along with the reconstructed flow into GRU to refine point-level flow. Extensive experiments on several different datasets show that our method can achieve promising performance.

翻译：三维场景流估计旨在估计两帧连续点云之间的逐点运动。超点，即具有相似几何特征的点，通常被用于捕捉三维场景中局部区域的相似运动，以进行场景流估计。然而，现有方法中，超点通过离线聚类方法生成，这难以较好地刻画复杂三维场景中具有相似运动的局部区域，从而导致场景流估计不准确。为此，我们提出了一种迭代式端到端的基于超点的场景流估计框架，其中超点可以动态更新以引导点级流预测。具体而言，我们的框架包括一个流引导的超点生成模块和一个超点引导的流修正模块。在超点生成模块中，我们利用前一次迭代的双向流信息来获取点与超点中心之间的匹配点对，从而构建软性的点到超点关联，进而生成成对点云的超点。利用生成的超点，我们首先通过自适应地聚合超点级流来重建每个点的流，然后编码成对点云中重建流之间的一致性。最后，我们将一致性编码与重建流共同输入GRU以修正点级流。在多个不同数据集上的广泛实验表明，我们的方法能够取得令人满意的性能。