Differentiable Convex Polyhedra Optimization from Multi-View Images
Abstract
This paper presents a novel approach for the differentiable rendering of convex polyhedra, addressing the limitations of recent methods that rely on implicit field supervision. Our technique introduces a strategy that combines non-differentiable computation of hyperplane intersection through duality transform with differentiable optimization for vertex positioning with three-plane intersection, enabling gradient-based optimization without the need for 3D implicit fields. This allows for efficient shape representation across a range of applications, from shape parsing to compact mesh reconstruction. This work not only overcomes the challenges of previous approaches but also sets a new standard for representing shapes with convex polyhedra.
Cite
Text
Ren et al. "Differentiable Convex Polyhedra Optimization from Multi-View Images." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-72673-6_14Markdown
[Ren et al. "Differentiable Convex Polyhedra Optimization from Multi-View Images." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/ren2024eccv-differentiable/) doi:10.1007/978-3-031-72673-6_14BibTeX
@inproceedings{ren2024eccv-differentiable,
title = {{Differentiable Convex Polyhedra Optimization from Multi-View Images}},
author = {Ren, Daxuan and Mei, Haiyi and Shi, Hezi and Zheng, Jianmin and Cai, Jianfei and Yang, Lei},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-72673-6_14},
url = {https://mlanthology.org/eccv/2024/ren2024eccv-differentiable/}
}