Abstract
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.
Abstract (translated)
三维点云的语义分割对于许多应用（如自动驾驶）非常重要。为了训练语义分割模型，标注良好的点云分割数据集是必不可少的。然而，点云标注对注释者来说非常耗时，通常需要调整相机视角并使用套索工具选择点。为减少点云标注的时间成本，我们提出了一种视角推荐方法来降低注释者的工作量。我们将Fitts定律应用于建模点云中套索选择的操作时间成本，并利用该模型确定最小化套索选择时间成本的视角。我们构建了一个用于三维点云语义分割的数据标注系统，该系统集成了我们的视角推荐方法。系统允许用户导航至推荐视角以实现高效的注释工作。通过消融研究，我们观察到这种方法显著降低了数据标注的时间成本，并且在不同数据集上与先前的视角选择方法进行了定性比较。
URL
https://arxiv.org/abs/2602.10871
