NeurIPS 2020poster7 citations
UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard, Edoardo Remelli, Pascal Fua
Abstract
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean structure of the 3D space objects exist in. In this paper, we show that building a geometry preserving 3-dimensional latent space helps the network concurrently learn global shape regularities and local reasoning in the object coordinate space and, as a result, boosts performance.
BibTeX
@inproceedings{NEURIPS2020_21327ba3,
author = {Guillard, Benoit and Remelli, Edoardo 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 = {3244--3253},
publisher = {Curran Associates, Inc.},
title = {UCLID-Net: Single View Reconstruction in Object Space},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/21327ba33b3689e713cdff1641128004-Paper.pdf},
volume = {33},
year = {2020}
}