DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting

• URL: http://arxiv.org/abs/2310.17632v1
• Date: Thu, 26 Oct 2023 17:50:10 GMT
• Title: DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting
• Authors: Kohei Yamashita, Shohei Nobuhara, Ko Nishino
• Abstract summary: We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task. We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps. A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function.
• Score: 35.18426818323455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometry reconstruction of textureless, non-Lambertian objects under unknown natural illumination (i.e., in the wild) remains challenging as correspondences cannot be established and the reflectance cannot be expressed in simple analytical forms. We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task. Unlike past methods that formulate this as inverse-rendering, i.e., estimation of reflectance, illumination, and geometry from images, our key idea is to realize that reflectance and illumination need not be disentangled and instead estimated as a compound reflectance map. We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps from the surface normals of the current geometry estimate and the input multi-view images. The network also explicitly estimates per-pixel confidence scores to handle global light transport effects. A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function using the recovered reflectance maps. By alternating between these two, and, most important, by bypassing the ill-posed problem of reflectance and illumination decomposition, the method accurately recovers object geometry in these challenging settings. Extensive experiments on both synthetic and real-world data clearly demonstrate its state-of-the-art accuracy.

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