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[arXiv]
[bibtex]@InProceedings{Schuster_2021_WACV, author = {Schuster, Rene and Unger, Christian and Stricker, Didier}, title = {A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {247-255} }
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
Abstract
Motion estimation is one of the core challenges in computer vision. With traditional dual-frame approaches, occlusions and out-of-view motions are a limiting factor, especially in the context of environmental perception for vehicles due to the large (ego-) motion of objects. Our work proposes a novel data-driven approach for temporal fusion of scene flow estimates in a multi-frame setup to overcome the issue of occlusion. Contrary to most previous methods, we do not rely on a constant motion model, but instead learn a generic temporal relation of motion from data. In a second step, a neural network combines bi-directional scene flow estimates from a common reference frame, yielding a refined estimate and a natural byproduct of occlusion masks. This way, our approach provides a fast multi-frame extension for a variety of scene flow estimators, which outperforms the underlying dual-frame approaches.
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