新城市激光雷达数据集，助力少标注下的点云分割研究
Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniques
- 构建1.43平方公里的空中激光雷达数据集，含近7000万点
- 在无训练标签情况下，通过半监督学习提升模型分割性能
- 适合自监督与半监督学习研究者使用，支持开放获取
3D语义分割在城市建模中至关重要，可实现对城市环境的精细理解与测绘。本文提出Turin3D：一个覆盖意大利都灵市中心约1.43平方公里的航空激光雷达数据集，包含近7000万点。我们描述了数据采集过程，并将Turin3D与其他已有数据集进行比较。由于标注过程复杂耗时，未对训练集进行完全标注；但对验证集和测试集进行了人工标注，以确保所提方法评估的可靠性。我们首先在现有数据集上训练的多个点云语义分割模型在Turin3D上的表现进行基准测试，随后利用未标注训练集，通过半监督学习技术提升其性能。该数据集将公开发布，支持室外点云分割研究，尤其适用于自监督与半监督学习方法。
原文摘要 · Abstract (English)
3D semantic segmentation plays a critical role in urban modelling, enabling detailed understanding and mapping of city environments. In this paper, we introduce Turin3D: a new aerial LiDAR dataset for point cloud semantic segmentation covering an area of around 1.43 km2 in the city centre of Turin with almost 70M points. We describe the data collection process and compare Turin3D with others previously proposed in the literature. We did not fully annotate the dataset due to the complexity and time-consuming nature of the process; however, a manual annotation process was performed on the validation and test sets, to enable a reliable evaluation of the proposed techniques. We first benchmark the performances of several point cloud semantic segmentation models, trained on the existing datasets, when tested on Turin3D, and then improve their performances by applying a semi-supervised learning technique leveraging the unlabelled training set. The dataset will be publicly available to support research in outdoor point cloud segmentation, with particular relevance for self-supervised and semi-supervised learning approaches given the absence of ground truth annotations for the training set.
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