ALISE: Annotation-Free LiDAR Instance Segmentation for Autonomous Driving

• URL: http://arxiv.org/abs/2510.05752v2
• Date: Fri, 10 Oct 2025 03:25:38 GMT
• Title: ALISE: Annotation-Free LiDAR Instance Segmentation for Autonomous Driving
• Authors: Yongxuan Lyu, Guangfeng Jiang, Hongsi Liu, Jun Liu,
• Abstract summary: We introduce ALISE, a novel framework that performs LiDAR instance segmentation without any annotations.<n>Our approach starts by employing Vision Foundation Models (VFMs), guided by text and images, to produce initial pseudo-labels.<n>We then refine these labels through a dedicated manual-temporal voting module, which combines 2D and 3D semantics for both offline and online optimization.<n>This comprehensive design results in significant performance gains, establishing a new state-of-the-art for unsupervised 3D instance segmentation.
• Score: 9.361724251990154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The manual annotation of outdoor LiDAR point clouds for instance segmentation is extremely costly and time-consuming. Current methods attempt to reduce this burden but still rely on some form of human labeling. To completely eliminate this dependency, we introduce ALISE, a novel framework that performs LiDAR instance segmentation without any annotations. The central challenge is to generate high-quality pseudo-labels in a fully unsupervised manner. Our approach starts by employing Vision Foundation Models (VFMs), guided by text and images, to produce initial pseudo-labels. We then refine these labels through a dedicated spatio-temporal voting module, which combines 2D and 3D semantics for both offline and online optimization. To achieve superior feature learning, we further introduce two forms of semantic supervision: a set of 2D prior-based losses that inject visual knowledge into the 3D network, and a novel prototype-based contrastive loss that builds a discriminative feature space by exploiting 3D semantic consistency. This comprehensive design results in significant performance gains, establishing a new state-of-the-art for unsupervised 3D instance segmentation. Remarkably, our approach even outperforms MWSIS, a method that operates with supervision from ground-truth (GT) 2D bounding boxes by a margin of 2.53% in mAP (50.95% vs. 48.42%).

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