Back to geometry: Efficient indoor space segmentation from point clouds by 2D–3D geometry constrains

This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for i...

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Autori principali:	Shengjun Tang, Junjie Huang, Benhe Cai, Han Du, Baoding Zhou, Zhigang Zhao, You Li, Weixi Wang, Renzhong Guo
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Elsevier 2024-12-01
Serie:	International Journal of Applied Earth Observations and Geoinformation
Soggetti:	Indoor segmentation Point cloud Geometry constrain Morphology-based segmentation Indoor modeling
Accesso online:	http://www.sciencedirect.com/science/article/pii/S1569843224006216
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