Uncertainty-aware LiDAR Panoptic Segmentation

• URL: http://arxiv.org/abs/2210.04472v1
• Date: Mon, 10 Oct 2022 07:54:57 GMT
• Title: Uncertainty-aware LiDAR Panoptic Segmentation
• Authors: Kshitij Sirohi, Sajad Marvi, Daniel B\"uscher and Wolfram Burgard
• Abstract summary: 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. We provide several strong baselines combining state-of-the-art panoptic segmentation networks with sampling-free uncertainty estimation techniques.
• Score: 21.89063036529791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: \url{https://github.com/kshitij3112/EvLPSNet}

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