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Scene flow is the three-dimensional (3D) motion field of a scene. It providesinformation about the spatial arrangement and rate of change of objects indynamic environments. Current learning-based approaches seek to estimate thescene flow directly from point clouds and have achieved state-of-the-artperformance. However, supervised learning methods are inherently domainspecific and require a large amount of labeled data. Annotation of scene flowon real-world point clouds is expensive and challenging, and the lack of suchdatasets has recently sparked interest in self-supervised learning methods. Howto accurately and robustly learn scene flow representations without labeledreal-world data is still an open problem. Here we present a simple andinterpretable objective function to recover the scene flow from point clouds.We use the graph Laplacian of a point cloud to regularize the scene flow to be"as-rigid-as-possible". Our proposed objective function can be used with orwithout learning---as a self-supervisory signal to learn scene flowrepresentations, or as a non-learning-based method in which the scene flow isoptimized during runtime. Our approach outperforms related works in manydatasets. We also show the immediate applications of our proposed method fortwo applications: motion segmentation and point cloud densification.

Scene Flow from Point Clouds with or without Learning