Multiframe Scene Flow with Piecewise Rigid Motion

Vladislav Golyanik, Kihwan Kim, Robert Maier, Matthias Nießner, Didier Stricker, Jan Kautz

In: 3DVision 2017 |. International Conference on 3DVision (3DV-17) 5th October 10-12 Qingdao China Conference Publishing Services (CPS) IEEE Xplore and CSDL 10/2017.


We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences. In contrast to the competing methods, we take advantage of an oversegmentation of the reference frame and robust optimization techniques. We formulate scene flow recovery as a global non-linear least squares problem which is iteratively solved by a damped Gauss-Newton approach. As a result, we obtain a qualitatively new level of accuracy in RGB-D based scene flow estimation which can potentially run in real-time. Our method can handle challenging cases with rigid, piecewise rigid, articulated and moderate non-rigid motion, and does not rely on prior knowledge about the types of motions and deformations. Extensive experiments on synthetic and real data show that our method outperforms state-of-the-art.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz