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DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

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Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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arXiv:1912.04302.pdf
(Preprint), 4MB

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Citation

Božič, A., Zollhöfer, M., Theobalt, C., & Nießner, M. (2019). DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data. Retrieved from http://arxiv.org/abs/1912.04302.


Cite as: http://hdl.handle.net/21.11116/0000-0005-7DDE-6
Abstract
Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. One recent approach proposes self-supervision based on non-rigid reconstruction. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a novel semi-supervised strategy to obtain dense inter-frame correspondences from a sparse set of annotations. This way, we obtain a large dataset of 400 scenes, over 390,000 RGB-D frames, and 2,537 densely aligned frame pairs; in addition, we provide a test set along with several metrics for evaluation. Based on this corpus, we introduce a data-driven non-rigid feature matching approach, which we integrate into an optimization-based reconstruction pipeline. Here, we propose a new neural network that operates on RGB-D frames, while maintaining robustness under large non-rigid deformations and producing accurate predictions. Our approach significantly outperforms both existing non-rigid reconstruction methods that do not use learned data terms, as well as learning-based approaches that only use self-supervision.