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In this work, we propose a holistic active learning (AL) approach to maximize model performance given limited annotation budgets. We investigate the appropriate sample granularity for active selection under the realistic \u201cclick\u201d measurement of annotation cost, and demonstrate that superpoint-based selection allows for most efficient usage of the limited budget, when compared with point-level, polygon-level and instance\/shape-level selection. We further propose new objective for AL acquisition function and exploit local consistency constraints to boost the performance of our superpoint-based approach. We evaluate our methods on three benchmark datasets, ShapeNet and PartNet and S3DIS. The results demonstrate that AL is an effective strategy to address the high annotation costs in semantic point cloud segmentation. <\/jats:p>","DOI":"10.1142\/s281103232440006x","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T05:41:22Z","timestamp":1717825282000},"source":"Crossref","is-referenced-by-count":3,"title":["Label-Efficient Point Cloud Semantic Segmentation: A Holistic Active Learning Approach"],"prefix":"10.1142","volume":"02","author":[{"given":"Xian","family":"Shi","sequence":"first","affiliation":[{"name":"South China University of Technology, Guangzhou, P. R. 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