ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation
Liu Y, Chen Y, Wang H, Belagiannis V, Reid I, Carneiro G (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15059 LNCS
Pages Range: 81-99
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Milan
ISBN: 9783031732317
DOI: 10.1007/978-3-031-73232-4_5
Abstract
The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples. This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2). IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Furthermore, our contrastive learning employs informative samples drawn from a distribution of positive and negative embeddings learned from the entire training set. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field. https://github.com/yyliu01/IT2.
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APA:
Liu, Y., Chen, Y., Wang, H., Belagiannis, V., Reid, I., & Carneiro, G. (2025). ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 81-99). Milan, IT: Springer Science and Business Media Deutschland GmbH.
MLA:
Liu, Yuyuan, et al. "ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation." Proceedings of the 18th European Conference on Computer Vision, ECCV 2024, Milan Ed. Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol, Springer Science and Business Media Deutschland GmbH, 2025. 81-99.
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