ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation
Liu, Yuyuan Chen, Yuanhong Wang, Hu Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo
Liu, Yuyuan
Chen, Yuanhong
Wang, Hu
Belagiannis, Vasileios
Reid, Ian
Carneiro, Gustavo
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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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.
Citation
Y. Liu, Y. Chen, H. Wang, V. Belagiannis, I. Reid, and G. Carneiro, “ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15059, pp. 81–99, Jan. 2025, doi: 10.1007/978-3-031-73232-4_5.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
European Conference on Computer Vision (ECCV)
Keywords
LiDAR Semantic Segmentation, Semi-supervised Learning
Subjects
Source
European Conference on Computer Vision (ECCV)
Publisher
Springer Nature