University of Oulu

M. Pedone, A. Mostafa and J. Heikkilä, "Learning non-rigid surface reconstruction from spatia-temporal image patches," 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 10134-10140, doi: 10.1109/ICPR48806.2021.9412352

Learning non-rigid surface reconstruction from spatio-temporal image patches

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Author: Pedone, Matteo1; Mostafa, Abdelrahman1; Heikkilä, Janne1
Organizations: 1Center for Machine Vision Research and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.7 MB)
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Language: English
Published: IEEE Computer Society, 2021
Publish Date: 2021-10-21


We present a method to reconstruct a dense spatiotemporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches.

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Series: International Conference on Pattern Recognition
ISSN: 1051-4651
ISSN-L: 1051-4651
ISBN: 978-1-7281-8808-9
ISBN Print: 978-1-7281-8809-6
Pages: 10134 - 10140
DOI: 10.1109/ICPR48806.2021.9412352
Host publication: 2020 25th International Conference on Pattern Recognition (ICPR)
Conference: International Conference on Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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