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Semantic-ITC: A Frame-wise Indoor Mobile Laser Scanning Dataset and Benchmark for Semantic Segmentation — arXiv:2609.18493

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18493
License (as archived): CC0-1.0
Archived: 2026-10-15T16:16:34.060Z
SHA-256 of the archived text: e6fa5353835e70fddc64af551c82e96024f91ebc1d9626158deb112891c0a79a
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How to cite

Semantic-ITC: A Frame-wise Indoor Mobile Laser Scanning Dataset and Benchmark for Semantic Segmentation — arXiv:2609.18493 — arXiv (en), https://arxiv.org/abs/2609.18493 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_080fc4c70a31 (Metraipolis archive dc_080fc4c70a31, sha256 e6fa5353835e70fddc64af551c82e96024f91ebc1d9626158deb112891c0a79a)

BibTeX

@misc{dc080fc4c70a31,
  title = {Semantic-ITC: A Frame-wise Indoor Mobile Laser Scanning Dataset and Benchmark for Semantic Segmentation — arXiv:2609.18493},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18493},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_080fc4c70a31; sha256 e6fa5353835e70fddc64af551c82e96024f91ebc1d9626158deb112891c0a79a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18493. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.18493 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T15:45:19.796Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).