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 ego-pose distance based sampling in the context of AL. A detailed presentation on the dataset is available here https://www.youtube.com/watch?v=5m6ALIs-s20.

翻译：自动驾驶感知如今高度依赖基于深度学习的架构，这类架构需要大规模标注数据集及其相关的采集与标注成本。三维语义数据对于障碍物检测和自车定位等核心感知任务至关重要。我们提出一个新数据集——Navya三维分割数据集（Navya3DSeg），其标签空间多样化，对应大规模量产级运营领域，涵盖来自13个国家的乡村、城区、工业场地和大学校园。该数据集包含23个带标注序列和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。我们还提出一种基于迭代多标签分层的新颖序列化数据集划分生成方法，实验表明该方法相较于SemanticKITTI数据集原始划分可实现+1.2%的平均交并比（mIoU）提升。我们使用最先进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（AL）的数据集蒸馏框架，并引入一种在AL背景下名为“基于自车姿态距离采样”的无启发式采样方法。数据集详细说明见 https://www.youtube.com/watch?v=5m6ALIs-s20。