Selection is a fundamental task in exploratory analysis and visualization of 3D point clouds. Prior researches on selection methods were developed mainly based on heuristics such as local point density, thus limiting their applicability in general data. Specific challenges root in the great variabilities implied by point clouds (e.g., dense vs. sparse), viewpoint (e.g., occluded vs. non-occluded), and lasso (e.g., small vs. large). In this work, we introduce LassoNet, a new deep neural network for lasso selection of 3D point clouds, attempting to learn a latent mapping from viewpoint and lasso to point cloud regions. To achieve this, we couple user-target points with viewpoint and lasso information through 3D coordinate transform and naive selection, and improve the method scalability via an intention filtering and farthest point sampling. A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data. We conduct a formal user study to compare LassoNet with two state-of-the-art lasso-selection methods. The evaluations confirm that our approach improves the selection effectiveness and efficiency across different combinations of 3D point clouds, viewpoints, and lasso selections. Project Website: https://lassonet.github.io

翻译：选择是3D点云探索性分析与可视化的基础任务。以往的选择方法主要基于局部点密度等启发式规则开发，因此限制了其在通用数据中的适用性。具体挑战源于点云（如密集与稀疏）、视角（如遮挡与非遮挡）和套索（如小区域与大区域）所蕴含的巨大变异性。本文提出LassoNet——一种用于3D点云套索选择的新型深度神经网络，旨在学习从视角和套索到点云区域的隐式映射。为此，我们通过3D坐标变换和朴素选择将用户目标点与视角、套索信息耦合，并利用意图过滤和最远点采样提升方法的可扩展性。我们基于两个不同点云数据集上超过3万条套索选择记录训练了一个层级化网络，并通过正式用户研究将LassoNet与两种先进套索选择方法进行对比。评估证实，我们的方法在不同3D点云、视角和套索选择组合下均提升了选择有效性和效率。项目网站：https://lassonet.github.io