用视觉模型推荐最佳视角，减少点云标注时间
Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling
- 基于菲茨定律建模选点耗时，推荐最优视角
- 实测可显著降低标注时间成本
- 适合点云数据标注与交互优化研究者
三维点云语义分割在自动驾驶等应用中至关重要，但标注过程耗时，需反复调整视角并使用套索选择点。为减少标注时间，本文提出一种视角推荐方法，通过适配菲茨定律建模套索选择的耗时，并推荐使该耗时最小的视角给标注员。我们构建了一个集成该方法的点云标注系统，支持导航至推荐视角以提升标注效率。消融实验表明，该方法能有效降低标注时间成本；定性对比也验证了其在多个数据集上的优越性。
原文摘要 · Abstract (English)
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.
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