Semantic-ITC：面向语义分割的逐帧室内移动激光扫描数据集与基准

中文摘要 AI 辅助

当前点云语义分割基准主要关注重建的室内场景或室外激光雷达感知，室内移动激光扫描（MLS）帧的语义标签在很大程度上仍然缺失。本文介绍了Semantic-ITC，据我们所知，这是首个用于逐帧室内MLS语义分割的公开数据集和基准。该数据集包含52个室内序列、79,108个MLS帧以及12.3亿个标记点，这些数据采集自教室、走廊、会议室、办公室和学习区域。标签直接附着于每帧中测量的激光雷达点，涵盖16个语义类别，包括结构元素、家具、房间设备、植被和其他室内物体。Semantic-ITC保留了室内MLS的稀疏、非均匀和逐帧采样模式，使其区别于场景级重建点云和基于网格的室内数据集。标注通过混合流程生成，该流程结合了应用于同步RGB图像的视觉基础模型的预测、来自BIM的结构信息以及人工细化，最终标签分配给原始激光雷达帧。提供了一个单帧基准，最佳基线达到79.27%的mIoU。剩余误差集中在物体边界和模糊的室内类别周围，表明在稀疏帧几何和长尾类别分布下室内MLS分割的挑战。该数据集为直接在测量的室内MLS帧上评估语义分割提供了公共基准，并支持未来对逐帧室内MLS语义分割的研究。

英文摘要

Semantic labels for indoor mobile laser scanning (MLS) frames remain largely absent from current point cloud semantic segmentation benchmarks, which mainly focus on reconstructed indoor scenes or outdoor LiDAR perception. This paper introduces Semantic-ITC, to the best of our knowledge the first public dataset and benchmark for frame-wise indoor MLS semantic segmentation. The dataset contains 52 indoor sequences, 79,108 MLS frames, and 1.23 billion labeled points collected in classrooms, corridors, meeting rooms, offices, and study areas. Labels are attached directly to measured LiDAR points in each frame using 16 semantic classes covering structural elements, furniture, room equipment, vegetation, and other indoor objects. Semantic-ITC preserves the sparse, non-uniform, and frame-wise sampling pattern of indoor MLS, making it distinct from scene-level reconstructed point clouds and mesh-based indoor datasets. The annotations are produced by a hybrid workflow that combines predictions from a visual foundation model applied to synchronized RGB images, structural information from BIM, and manual refinement, with the final labels assigned to the original LiDAR frames. A single-frame benchmark is provided, and the best baseline reaches 79.27\% mIoU. Remaining errors are concentrated around object boundaries and ambiguous indoor classes, indicating the challenges of indoor MLS segmentation under sparse frame geometry and long-tailed class distributions. The dataset provides a public benchmark for evaluating semantic segmentation directly on measured indoor MLS frames and supports future studies on frame-wise indoor MLS semantic segmentation.

发表机构

• University of Twente（特文特大学）

机构由 AI 辅助整理，请以论文原文为准。