A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions

• URL: http://arxiv.org/abs/2011.01603v2
• Date: Wed, 4 Nov 2020 09:13:32 GMT
• Title: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
• Authors: Ren\'e Schuster, Christian Unger, Didier Stricker
• Abstract summary: We propose a novel data-driven approach for temporal fusion of scene flow estimates in a multi-frame setup. In a second step, a neural network combines bi-directional scene flow estimates from a common reference frame, yielding a refined estimate. This way, our approach provides a fast multi-frame extension for a variety of scene flow estimators, which outperforms the underlying dual-frame approaches.
• Score: 17.66624674542256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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