Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation

Bin Yang; Alexandru Paul Condurache

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arXiv · 2605.17135

Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation

Bin Yang·Alexandru Paul Condurache

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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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Bin Yang, Alexandru Paul Condurache. 2026-05-16. Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation. https://arxiv.org/abs/2605.17135

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