ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch
Abstract
We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives. ParSeNet is trained on a large-scale dataset of man-made 3D shapes and captures high-level semantic priors for shape decomposition. It handles a much richer class of primitives than prior work, and allows us to represent surfaces with higher fidelity. It also produces repeatable and robust parametrizations of a surface compared to purely geometric approaches. We present extensive experiments to validate our approach against analytical and learning-based alternatives. Our source code is publicly available at: https://hippogriff.github.io/parsenet."
BibTeX
@inproceedings{eccv2020_parsenetaparamet,
title = {ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds},
author = {Gopal Sharma and Difan Liu and Subhransu Maji and Evangelos Kalogerakis and Siddhartha Chaudhuri and Radomír Měch},
booktitle = {ECCV 2020},
year = {2020}
}