Title
Deep 3d Capture: Geometry And Reflectance From Sparse Multi-View Images
Abstract
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
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 Bi1635.28
Zexiang Xu210110.17
Kalyan Sunkavalli350031.75
David Kriegman47693451.96
Ravi Ramamoorthi54481237.21