ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation

• URL: http://arxiv.org/abs/2107.11769v1
• Date: Sun, 25 Jul 2021 09:40:48 GMT
• Title: ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
• Authors: Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, Winston H. Hsu
• Abstract summary: ReDAL aims to automatically select only informative and diverse sub-scene regions for label acquisition. A diversity-aware selection algorithm is also developed to avoid redundant annotations. Experiments show that our method highly outperforms previous active learning strategies.
• Score: 28.478555264574865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge. To reduce the huge annotation burden, we propose a Region-based and Diversity-aware Active Learning (ReDAL), a general framework for many deep learning approaches, aiming to automatically select only informative and diverse sub-scene regions for label acquisition. Observing that only a small portion of annotated regions are sufficient for 3D scene understanding with deep learning, we use softmax entropy, color discontinuity, and structural complexity to measure the information of sub-scene regions. A diversity-aware selection algorithm is also developed to avoid redundant annotations resulting from selecting informative but similar regions in a querying batch. Extensive experiments show that our method highly outperforms previous active learning strategies, and we achieve the performance of 90% fully supervised learning, while less than 15% and 5% annotations are required on S3DIS and SemanticKITTI datasets, respectively.

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