MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow

René Schuster, Christian Unger, Didier Stricker

In: Computer Science in Cars Symposium. ACM Computer Science in Cars Symposium (CSCS-2020) December 2-2 Feldkirchen Germany ACM 2020.


Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i.e. using a single sensor. Towards this end, we exploit the latest achievements in single image depth estimation, optical flow, and sparse-to-dense interpolation and propose a monocular combination approach (MonoComb) to compute dense scene flow. MonoComb uses optical flow to relate reconstructed 3D positions over time and interpolates occluded areas. This way, existing monocular methods are outperformed in dynamic foreground regions which leads to the second best result among the competitors on the challenging KITTI 2015 scene flow benchmark.

schuster2020mono.pdf (pdf, 5 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence