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.
@article{arxiv.2003.12181,
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},
journal= {arXiv preprint arXiv:2003.12181},
year = {2020}
}