论文标题
Pointstrinet：3D点集的学习三角剖分
PointTriNet: Learned Triangulation of 3D Point Sets
论文作者
论文摘要
这项工作考虑了几何深度学习中的一项新任务：在3D空间中的一组点之间产生三角剖分。我们提出了PointTrinet，这是一种可区分和可扩展的方法，使点将三角剖分设置为3D学习管道中的一层。该方法迭代应用两个神经网络：分类网络预测候选三角是否应出现在三角剖分中，而建议网络则建议其他候选者。这两个网络均使用新型三角形的输入编码来构成在附近点和三角形上的点网。由于这些学习问题在局部几何数据上有效，因此我们的方法是有效且可扩展的，并且可以概括地看不见形状类别。我们的网络是从表示为点云的一系列形状中以无监督的方式进行训练的。我们证明了这种方法对经典网格划分任务，对异常值的鲁棒性以及端到端学习系统中的组成部分的有效性。
This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable approach enabling point set triangulation as a layer in 3D learning pipelines. The method iteratively applies two neural networks: a classification network predicts whether a candidate triangle should appear in the triangulation, while a proposal network suggests additional candidates. Both networks are structured as PointNets over nearby points and triangles, using a novel triangle-relative input encoding. Since these learning problems operate on local geometric data, our method is efficient and scalable, and generalizes to unseen shape categories. Our networks are trained in an unsupervised manner from a collection of shapes represented as point clouds. We demonstrate the effectiveness of this approach for classical meshing tasks, robustness to outliers, and as a component in end-to-end learning systems.
