Uncertainty-aware LiDAR Panoptic Segmentation
This work addresses the need for reliable scene understanding in autonomous systems by providing uncertainty estimates, which is an incremental improvement over existing panoptic segmentation methods.
The paper tackles the problem of uncertainty-aware panoptic segmentation in LiDAR point clouds for autonomous driving, introducing EvLPSNet as the first sampling-free method that achieves the best performance in uncertainty quality and calibration compared to strong baselines.
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