论文标题
不确定性感知的LIDAR全腹分段
Uncertainty-aware LiDAR Panoptic Segmentation
论文作者
论文摘要
现代自主系统通常依赖于激光扫描仪，特别是自主驾驶场景。在这种情况下，可靠的场景理解是必不可少的。当前基于学习的方法通常试图实现此任务的最高绩效，同时忽略了对相关的不确定性的正确估计。在这项工作中，我们介绍了一种新的方法，用于解决使用激光点云的不确定性吸引全景分割的任务。我们提出的EVLPSNET网络是第一个以无抽样方式有效地解决此任务的人。它旨在预测每个点语义和实例分割，以及每点不确定性估计。此外，它结合了通过采用预测的不确定性来改善性能的方法。我们提供了几个强大的基准，将最新的泛圆形分割网络与无抽样的不确定性估计技术相结合。广泛的评估表明，与这些基线相比，我们在不确定性吸引的综合分割质量和校准方面取得了最佳性能。我们在以下网址提供代码
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
