Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling.
IEEE Trans Vis Comput Graph
; PP2024 Mar 22.
Article
in En
| MEDLINE
| ID: mdl-38517726
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 with a lasso tool. 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 a user 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
IEEE Trans Vis Comput Graph
Journal subject:
INFORMATICA MEDICA
Year:
2024
Document type:
Article