FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation
This work addresses scene flow estimation for 3D vision applications, presenting an incremental improvement with a new benchmark for dynamic 3D reconstruction.
The paper tackles scene flow estimation by incorporating geometric constraints into FlowNet3D, improving accuracy from 57.85% to 63.43% and reducing reconstruction error by up to 15.0% over FlowNet3D.
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.