Multiframe Scene Flow with Piecewise Rigid Motion
This work addresses scene flow estimation for computer vision applications, offering improved accuracy in handling rigid, piecewise rigid, articulated, and moderate non-rigid motions without prior knowledge.
The paper tackles the problem of scene flow estimation from RGB-D sequences by jointly optimizing patch appearances and local rigid motions, achieving a new level of accuracy and outperforming state-of-the-art methods in experiments on synthetic and real data.
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.