论文标题
parsenet：3D点云的参数表面拟合网络
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
论文作者
论文摘要
我们提出了一种新型的，可端到端的可训练的深层网络，称为parsenet，该网络将3D点云分解为参数表面贴片，包括B型平面贴片以及基本的几何原始图。 Parsenet在大型的人造3D形状数据集上进行了训练，并捕获了高级语义先验的形状分解。它处理比先前的工作要多得多的原语，并允许我们代表更高的保真度的表面。与纯几何方法相比，它还产生表面可重复且鲁棒的参数化。我们提出了广泛的实验，以验证我们的方法，以防止基于分析和学习的替代方案。我们的源代码可公开可用：https：//hippogriff.github.io/parsenet。
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.
