  Scene flow is the task of estimating 3D motion vectors to individual points
of a dynamic 3D scene. Motion vectors have shown to be beneficial for
downstream tasks such as action classification and collision avoidance.
However, data collected via LiDAR sensors and stereo cameras are computation
and labor intensive to precisely annotate for scene flow. We address this
annotation bottleneck on two ends. We propose a 3D scene flow benchmark and a
novel self-supervised setup for training flow models. The benchmark consists of
datasets designed to study individual aspects of flow estimation in progressive
order of complexity, from a single object in motion to real-world scenes.
Furthermore, we introduce Adversarial Metric Learning for self-supervised flow
estimation. The flow model is fed with sequences of point clouds to perform
flow estimation. A second model learns a latent metric to distinguish between
the points translated by the flow estimations and the target point cloud. This
latent metric is learned via a Multi-Scale Triplet loss, which uses
intermediary feature vectors for the loss calculation. We use our proposed
benchmark to draw insights about the performance of the baselines and of
different models when trained using our setup. We find that our setup is able
to keep motion coherence and preserve local geometries, which many
self-supervised baselines fail to grasp. Dealing with occlusions, on the other
hand, is still an open challenge.
