# Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning Source: [Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:31:45.237Z Authors: Rohit Mohan, Julia Hindel, Florian Drews, Claudius Gl\\"aser, Daniele Cattaneo, Abhinav Valada Published: 2025-06-16T09:03:51.000Z ## Abstract Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on closed-set assumptions, failing to detect unknown object instances. In this work, we propose ULOPS, an uncertainty-guided open-set panoptic segmentation framework that leverages Dirichlet-based evidential learning to model predictive uncertainty. Our architecture incorporates separate decoders for semantic segmentation with uncertainty estimation, embedding with prototype association, and instance center prediction. During inference, we leverage uncertainty estimates to identify and segment unknown instances. To strengthen the model's ability to differentiate between known and unknown objects, we introduce three uncertainty-driven loss functions. Uniform Evidence Loss to encourage high uncertainty in unknown regions. Adaptive Uncertainty Separation Loss ensures a consistent difference in uncertainty estimates between known and unknown objects at a global scale. Contrastive Uncertainty Loss refines this separation at the fine-grained level. To evaluate open-set performance, we extend benchmark settings on KITTI-360 and introduce a new open-set evaluation for nuScenes. Extensive experiments demonstrate that ULOPS consistently outperforms existing open-set LiDAR panoptic segmentation methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on SemanticTHAB (test)](): Semantic segmentation; SemanticTHAB (test); IoU (Person): 71.9. - [Semantic Segmentation on Panoptic-CUDAL (test)](): Semantic segmentation; Panoptic-CUDAL (test); IoU (Person): 0. ## Original sources and implementation [Original paper]() [PDF]()