# Uncertainty-aware LiDAR Panoptic Segmentation Source: [Uncertainty-aware LiDAR Panoptic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:38:22.399Z Authors: Kshitij Sirohi, Sajad Marvi, Daniel B\\"uscher, Wolfram Burgard Published: 2022-10-10T07:54:57.000Z ## Abstract Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties. In this work, we introduce a novel approach for solving the task of uncertainty-aware panoptic segmentation using LiDAR point clouds. Our proposed EvLPSNet network is the first to solve this task efficiently in a sampling-free manner. It aims to predict per-point semantic and instance segmentations, together with per-point uncertainty estimates. Moreover, it incorporates methods for improving the performance by employing the predicted uncertainties. We provide several strong baselines combining state-of-the-art panoptic segmentation networks with sampling-free uncertainty estimation techniques. Extensive evaluations show that we achieve the best performance on uncertainty-aware panoptic segmentation quality and calibration compared to these baselines. We make our code available at: https://github.com/kshitij3112/EvLPSNet ## 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 Panoptic-CUDAL (test)](): Semantic segmentation; Panoptic-CUDAL (test); IoU (Person): 0. - [Semantic Segmentation on SemanticTHAB (test)](): Semantic segmentation; SemanticTHAB (test); IoU (Person): 70.3. ## Original sources and implementation [Original paper]() [PDF]()