超点引导的自监督三维场景流估计
Self-Supervised 3D Scene Flow Estimation Guided by Superpoints
AI总结：
针对现有超点生成方法难以适配复杂3D场景导致场景流估计不准的问题，提出迭代式端到端超点引导的自监督3D场景流估计框架，动态更新超点并结合双向流、GRU精化点级流，在多数据集上取得优异性能。
摘要：
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，以服务于场景流估计。然而，现有方法中的超点通过离线聚类方法生成，无法很好地刻画复杂三维场景中具有相似运动的局部区域，导致场景流估计不准确。为此，我们提出一种基于超点的迭代式端到端场景流估计框架，其中超点可被动态更新以引导点级流预测。具体而言，该框架包含流引导的超点生成模块和超点引导的流精化模块。在超点生成模块中，我们利用上一次迭代的双向流信息获取点与超点中心的匹配点，以构建软点-超点关联，从而为成对点云生成超点。基于生成的超点，我们首先通过自适应聚合超点级流来重建每个点的流，随后编码成对点云重建流之间的一致性。最后，我们将一致性编码与重建流一同输入GRU（门控循环单元）以精化点级流。在多个不同数据集上开展的大量实验表明，我们的方法能够取得优异的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
