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.

本文提出了一种基于可动态更新超级点的 3D 场景流估计框架，该框架结合超级点生成模块和超级点导向流细化模块，通过流提导超级点的生成和可适应聚合超级点级流来重建点的流，并将一致性编码和重建流输入 GRU 以细化点级流，实现了对复杂 3D 场景的准确估计。