Deep Photometric Stereo on a Sunny Day
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Photometric Stereo in outdoor illumination remains a challenging, ill-posed problem. Indeed, it has been shown that the scene structure cannot be recovered unambiguously from the photometric cues alone when it is lit by the sun over the course of a single day. In this paper, we present a CNN-based technique for photometric stereo on a single sunny day. We solve the ambiguity issue by combining photometric cues with prior knowledge on material properties, local surface geometry and the natural variations in outdoor lighting. To train the CNN, we create a dataset of realistic synthetic renders with both diffuse and specular materials. Given eight outdoor images taken during a single sunny day, our method robustly estimates the scene surface normals. Our approach does not require precise geolocation to work and significantly outperforms several state-of-the-art methods on images with real lighting. This shows that our CNN can combine efficiently learned priors and photometric cues available during a single sunny day.
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