Autonomous driving (AD) perception today relies heavily on deep learning based architectures requiring large scale annotated datasets with their associated costs for curation and annotation. The 3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain, including rural, urban, industrial sites and universities from 13 countries. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds. We also propose a novel method for sequential dataset split generation based on iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU improvement over the original split proposed by SemanticKITTI dataset. A complete benchmark for semantic segmentation task was performed, with state of the art methods. Finally, we demonstrate an active learning (AL) based dataset distillation framework. We introduce a novel heuristic-free sampling method called distance sampling in the context of AL. A detailed presentation on the dataset is available at https://www.youtube.com/watch?v=5m6ALIs-s20 .

翻译：自主驾驶感知技术当前高度依赖基于深度学习的架构，此类架构需要大规模标注数据集，并伴随相应的数据整理与标注成本。三维语义数据在障碍物检测、自车定位等核心感知任务中具有重要作用。我们提出一个新数据集Navya三维分割（Navya3DSeg），其包含丰富的标签空间，覆盖来自13个国家的乡村、城区、工业园区及大学等大规模生产级运营场景。该数据集包含23个已标注序列与25个无标注补充序列，旨在探索基于点云的自监督与半监督语义分割基准测试。我们还提出一种基于迭代多标签分层策略的序列数据集分割生成新方法，实验表明该方法相较于SemanticKITTI数据集原始分割方案可获得平均交并比（mIoU）1.2%的提升。我们利用现有最优方法完成了完整的语义分割任务基准测试。最后，我们提出一种基于主动学习的数据集精炼框架，并在该框架中引入一种名为距离采样的无启发式采样方法。数据集详细介绍可参阅 https://www.youtube.com/watch?v=5m6ALIs-s20。