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Differentiable Stereopsis: Meshes from multiple views using differentiable rendering

Goel, Shubham and Gkioxari, Georgia and Malik, Jitendra (2022) Differentiable Stereopsis: Meshes from multiple views using differentiable rendering. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, pp. 8625-8634. ISBN 978-1-6654-6946-3.

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We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and modern differentiable rendering to build an end-to-end model which predicts textured 3D meshes of objects with varying topologies and shape. We frame stereopsis as an optimization problem and simultaneously update shape and cameras via simple gradient descent. We run an extensive quantitative analysis and compare to traditional multi-view stereo techniques and state-of-the-art learning based methods. We show compelling reconstructions on challenging real-world scenes and for an abundance of object types with complex shape, topology and texture. Project webpage:

Item Type:Book Section
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URLURL TypeDescription ItemDiscussion Paper
Goel, Shubham0000-0002-1700-939X
Malik, Jitendra0000-0003-3695-1580
Record Number:CaltechAUTHORS:20221215-789792000.24
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118381
Deposited By: George Porter
Deposited On:19 Dec 2022 20:34
Last Modified:19 Dec 2022 23:14

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