  We present FlowNet3D++, a deep scene flow estimation network. Inspired by
classical methods, FlowNet3D++ incorporates geometric constraints in the form
of point-to-plane distance and angular alignment between individual vectors in
the flow field, into FlowNet3D. We demonstrate that the addition of these
geometric loss terms improves the previous state-of-art FlowNet3D accuracy from
57.85% to 63.43%. To further demonstrate the effectiveness of our geometric
constraints, we propose a benchmark for flow estimation on the task of dynamic
3D reconstruction, thus providing a more holistic and practical measure of
performance than the breakdown of individual metrics previously used to
evaluate scene flow. This is made possible through the contribution of a novel
pipeline to integrate point-based scene flow predictions into a global dense
volume. FlowNet3D++ achieves up to a 15.0% reduction in reconstruction error
over FlowNet3D, and up to a 35.2% improvement over KillingFusion alone. We will
release our scene flow estimation code later.
