Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction
Abstract
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution. Our graph-theoretic formulation models the spatio-temporal relationships among our observations in terms of the joint estimation of their 3D geometry and its discrete Laplace operator. Towards this end, we define a tri-convex optimization framework that leverages the geometric properties and dependencies found among a Euclidean shape-space and the discrete Laplace operator describing its local and global topology. We present a reconstructability analysis, experiments on motion capture data and multi-view image datasets, as well as explore applications to geometry-based event segmentation and data association.
Cite
Text
Xu and Dunn. "Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00163Markdown
[Xu and Dunn. "Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/xu2019iccv-discrete/) doi:10.1109/ICCV.2019.00163BibTeX
@inproceedings{xu2019iccv-discrete,
title = {{Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction}},
author = {Xu, Xiangyu and Dunn, Enrique},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00163},
url = {https://mlanthology.org/iccv/2019/xu2019iccv-discrete/}
}