RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification

• URL: http://arxiv.org/abs/2408.17143v1
• Date: Fri, 30 Aug 2024 09:34:36 GMT
• Title: RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification
• Authors: Nikolina Kubiak, Elliot Wortman, Armin Mustafa, Graeme Phillipson, Stephen Jolly, Simon Hadfield,
• Abstract summary: Existing shadow detection models struggle to differentiate dark image areas from shadows. In this paper, we tackle this issue by verifying that all detected shadows are real, i.e. they have paired shadow casters. We perform this step in a physically-accurate manner by differentiably re-rendering the scene and observing the changes stemming from carving out estimated shadow casters. Thanks to this approach, the RenDetNet proposed in this paper is the first learning-based shadow detection model whose supervisory signals can be computed in a self-supervised manner.
• Score: 15.68136544586505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing shadow detection models struggle to differentiate dark image areas from shadows. In this paper, we tackle this issue by verifying that all detected shadows are real, i.e. they have paired shadow casters. We perform this step in a physically-accurate manner by differentiably re-rendering the scene and observing the changes stemming from carving out estimated shadow casters. Thanks to this approach, the RenDetNet proposed in this paper is the first learning-based shadow detection model whose supervisory signals can be computed in a self-supervised manner. The developed system compares favourably against recent models trained on our data. As part of this publication, we release our code on github.

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