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Title: Differentiable Appearance Acquisition from a Flash/No-flash RGB-D Pair
Reconstructing 3D objects in natural environments requires solving the ill-posed problem of geometry, spatially-varying material, and lighting estimation. As such, many approaches impractically constrain to a dark environment, use controlled lighting rigs, or use few handheld captures but suffer reduced quality. We develop a method that uses just two smartphone exposures captured in ambient lighting to reconstruct appearance more accurately and practically than baseline methods. Our insight is that we can use a flash/no-flash RGB-D pair to pose an inverse rendering problem using point lighting. This allows efficient differentiable rendering to optimize depth and normals from a good initialization and so also the simultaneous optimization of diffuse environment illumination and SVBRDF material. We find that this reduces diffuse albedo error by 25%, specular error by 46%, and normal error by 30% against single and paired-image baselines that use learning-based techniques. Given that our approach is practical for everyday solid objects, we enable photorealistic relighting for mobile photography and easier content creation for augmented reality.  more » « less
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; ; ; ; ;
Date Published:
Journal Name:
International Conference on Computational Photography
Page Range / eLocation ID:
1 to 12
Medium: X
Sponsoring Org:
National Science Foundation
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