Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2605.17135v1
• Date: Sat, 16 May 2026 19:58:41 GMT
• Title: Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
• Abstract summary: Collaborative Learning for LiDAR Semi-supervised segmentation (CoLLiS)<n>CoLLiS trains multiple representations collaboratively in a single step by treating them as coequal students.<n>Experiments on three datasets demonstrate that CoLLiS consistently outperforms state-of-the-art LiDAR SemiSL methods.
• Score: 51.15174185939441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Annotating large-scale LiDAR point clouds for 3D semantic segmentation is costly and time-consuming, which motivates the use of semi-supervised learning (SemiSL). Standard LiDAR SemiSL methods typically adopt a two-step training paradigm, where pseudo-labels are separately generated from a single distillation source, either from the same or another LiDAR representation. Such supervision relies on a unique source of pseudo-labels, which can reinforce confirmation bias and propagate errors during training, ultimately limiting performance. To address this challenge, we introduce CoLLiS, a novel framework that leverages Collaborative Learning for LiDAR Semi-supervised segmentation. Unlike prior paradigms with decoupled pseudo-labeling and training phases, CoLLiS trains multiple representations collaboratively in a single step by treating them as coequal students. Each student is adaptively distilled from multiple representations, while inter-student disparities are monitored online to resolve contradictory supervision and effectively mitigate confirmation bias. Extensive experiments on three datasets demonstrate that CoLLiS consistently outperforms state-of-the-art LiDAR SemiSL methods, with particularly strong gains in low-label regimes.

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