Sensor-Guided Optical Flow

• URL: http://arxiv.org/abs/2109.15321v1
• Date: Thu, 30 Sep 2021 17:59:57 GMT
• Title: Sensor-Guided Optical Flow
• Authors: Matteo Poggi, Filippo Aleotti, Stefano Mattoccia
• Abstract summary: This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy on known or unseen domains. We show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms.
• Score: 53.295332513139925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy either on known or unseen domains. Given the availability of sparse yet accurate optical flow hints from an external source, these are injected to modulate the correlation scores computed by a state-of-the-art optical flow network and guide it towards more accurate predictions. Although no real sensor can provide sparse flow hints, we show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms, leading to accurate enough hints for our purpose. Experimental results with a state-of-the-art flow network on standard benchmarks support the effectiveness of our framework, both in simulated and real conditions.

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