LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit this new availability of data and continue to rely on the same classical datasets. Different ways of performing LIDAR semantic segmentation training and inference can be divided into several subfields, which include the following: domain generalization, the ability to segment data coming from unseen domains ; source-to-source segmentation, the ability to segment data coming from the training domain; and pre-training, the ability to create re-usable geometric primitives. In this work, we aim to improve results in all of these subfields with the novel approach of multi-source training. Multi-source training relies on the availability of various datasets at training time and uses them together rather than relying on only one dataset. To overcome the common obstacles found for multi-source training, we introduce the coarse labels and call the newly created multi-source dataset COLA. We propose three applications of this new dataset that display systematic improvement over single-source strategies: COLA-DG for domain generalization (up to +10%), COLA-S2S for source-to-source segmentation (up to +5.3%), and COLA-PT for pre-training (up to +12%).

翻译：摘要：近年来，面向自动驾驶的激光雷达语义分割已成为一个日益引人关注的研究领域。相关数据集与方法迅速涌现并扩展，但现有方法尚未充分利用这些新数据的可用性，仍依赖传统经典数据集。激光雷达语义分割的训练与推理可划分为多个子领域，包括：域泛化（对未见域数据的分割能力）、源到源分割（对训练域数据的分割能力）以及预训练（创建可复用几何基元的能力）。本研究旨在通过创新的多源训练方法全面提升上述子领域的性能。多源训练利用训练阶段可获取的多个数据集进行联合学习，而非仅依赖单一数据集。为克服多源训练中常见的障碍，我们引入粗标签概念，并将新构建的多源数据集命名为COLA。我们提出该数据集的三种应用方案，相较单源策略均展现出系统性提升：COLA-DG用于域泛化（提升高达+10%）、COLA-S2S用于源到源分割（提升高达+5.3%）、COLA-PT用于预训练（提升高达+12%）。