Uncertainty-aware LiDAR Panoptic Segmentation

Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Conventional learning-based methods generally 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 that utilize the uncertainties to improve the segmentation performance. We provide several strong baselines combining state-of- the-art LiDAR 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