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Research Article | Open Access

Active self-training for weakly supervised 3D scene semantic segmentation

College of Computer Science & Software Engineering,Shenzhen University, Shenzhen 518060, China
School of Computer Science, Carleton University, Ottawa K1S 5B6, Canada
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Graphical Abstract

Abstract

Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to learn from only a small fraction of data. These methods are typically based on learning with contrastive losses while automatically deriving per-point pseudo-labels from a sparse set of user-annotated labels. In this paper, our key observation is that the selection of which samples to annotate is as important as how these samples are used for training. Thus, we introduce a method for weakly supervised segmentation of 3D scenes that combines self-training with active learning. Active learning selects points for annotation that are likely to result in improvements to the trained model, while self-training makes efficient use of the user-provided labels for learning the model. We demonstrate that our approach leads to an effective method that provides improvements in scene segmentation over previous work and baselines, while requiring only a few user annotations.

Keywords

semantic segmentationactive learningweakly supervisedself-training

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Computational Visual Media
Volume 10 Issue 3,
June 2024
Pages 425-438
DOI: 10.1007/s41095-022-0311-7
Cite this article:
Liu G, van Kaick O, Huang H, et al. Active self-training for weakly supervised 3D scene semantic segmentation. Computational Visual Media, 2024, 10(3): 425-438. https://doi.org/10.1007/s41095-022-0311-7

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Received: 16 June 2022
Accepted: 04 September 2022
Published: 22 March 2024
© The Author(s) 2024.

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