Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion. In this work we present an alternative method for end-to-end scene flow learning by joint estimation of non-rigid residual flow and ego-motion flow for dynamic 3D scenes. We propose to learn the relative rigid transformation from a pair of point clouds followed by an iterative refinement. We then learn the non-rigid flow from transformed inputs with the deducted rigid part of the flow. Furthermore, we extend the supervised framework with self-supervisory signals based on the temporal consistency property of a point cloud sequence. Our solution allows both training in a supervised mode complemented by self-supervisory loss terms as well as training in a fully self-supervised mode. We demonstrate that decomposition of scene flow into non-rigid flow and ego-motion flow along with an introduction of the self-supervisory signals allowed us to outperform the current state-of-the-art supervised methods.

中文翻译：

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非刚性残余流和自我运动的自监督学习

大多数当前的场景流方法选择将场景流建模为每点平移向量，而不区分 3D 运动的静态和动态分量。在这项工作中，我们通过联合估计动态 3D 场景的非刚性残余流和自我运动流，提出了一种端到端场景流学习的替代方法。我们建议从一对点云中学习相对刚性的变换，然后进行迭代细化。然后，我们从转换后的输入中学习非刚性流，并减去流的刚性部分。此外，我们基于点云序列的时间一致性属性使用自监督信号扩展了监督框架。我们的解决方案允许在监督模式下进行训练，并以自我监督损失项作为补充，也允许在完全自我监督模式下进行训练。我们证明了将场景流分解为非刚性流和自我运动流以及引入自我监督信号使我们能够胜过当前最先进的监督方法。