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arXiv · 2003.12181

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

Gopal Sharma·Difan Liu·Subhransu Maji·Evangelos Kalogerakis·Siddhartha Chaudhuri·Radomír Měch
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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.

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Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch. 2020-03-26. ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds. https://arxiv.org/abs/2003.12181

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