Abstract | ||
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We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only' six images captured by wide-baseline cameras under collocated point lighting. We first estimate per-view depth maps using a deep multi-view stereo network; these depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture that is trained to pool features from these coarsely aligned images and predict per-view spatially-varying diffuse albedo, surface normals, specular roughness and specular albedo. Finally, we fuse and refine these per-view estimates to construct high-quality geometry and per-vertex BRDFs. We do this by jointly optimizing the latent space of our multi-view reflectance network to minimize the photometric error between images rendered with our predictions and the input images. While previous state-of-the-art methods fail on such sparse acquisition setups, we demonstrate, via extensive experiments on synthetic and real data, that our method produces high-quality reconstructions that can be used to render photorealistic images. |
Year | DOI | Venue |
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2020 | 10.1109/CVPR42600.2020.00600 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 3 |
PageRank | References | Authors |
0.37 | 33 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sai Bi | 1 | 63 | 5.28 |
Zexiang Xu | 2 | 101 | 10.17 |
Kalyan Sunkavalli | 3 | 500 | 31.75 |
David Kriegman | 4 | 7693 | 451.96 |
Ravi Ramamoorthi | 5 | 4481 | 237.21 |