Semantic segmentation of 3D point clouds is important for many applications, such as autonomous driving. To train semantic segmentation models, labeled point cloud segmentation datasets are essential. Meanwhile, point cloud labeling is time-consuming for annotators, which typically involves tuning the camera viewpoint and selecting points by lasso. To reduce the time cost of point cloud labeling, we propose a viewpoint recommendation approach to reduce annotators' labeling time costs. We adapt Fitts' law to model the time cost of lasso selection in point clouds. Using the modeled time cost, the viewpoint that minimizes the lasso selection time cost is recommended to the annotator. We build a data labeling system for semantic segmentation of 3D point clouds that integrates our viewpoint recommendation approach. The system enables users to navigate to recommended viewpoints for efficient annotation. Through an ablation study, we observed that our approach effectively reduced the data labeling time cost. We also qualitatively compare our approach with previous viewpoint selection approaches on different datasets.

翻译：三维点云的语义分割对于自动驾驶等众多应用至关重要。训练语义分割模型需要已标注的点云分割数据集。然而，点云标注过程对标注人员而言耗时费力，通常涉及调整相机视角并通过套索工具选择点云。为降低点云标注的时间成本，本文提出一种视角推荐方法以减少标注人员的标注耗时。我们通过适配费茨定律来建模点云中套索选择操作的时间成本。基于该时间成本模型，系统可向标注者推荐能够最小化套索选择时间成本的观测视角。我们构建了一个融合视角推荐功能的三维点云语义分割数据标注系统，该系统支持用户导航至推荐视角以实现高效标注。消融实验表明，本方法能有效降低数据标注的时间成本。我们还在不同数据集上对本方法与已有视角选择方法进行了定性对比分析。