NeurIPS 2020spotlight171 citations
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
Abstract
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable parameterization that is not limited in resolution.
BibTeX
@inproceedings{NEURIPS2020_fe40fb94,
author = {Remelli, Edoardo and Lukoianov, Artem and Richter, Stephan and Guillard, Benoit and Bagautdinov, Timur and Baque, Pierre and Fua, Pascal},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {22468--22478},
publisher = {Curran Associates, Inc.},
title = {MeshSDF: Differentiable Iso-Surface Extraction},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/fe40fb944ee700392ed51bfe84dd4e3d-Paper.pdf},
volume = {33},
year = {2020}
}