SEMANTIC SEGMENTATION OF INDOOR POINT CLOUDS USING CONVOLUTIONAL NEURAL NETWORK

11 citations as recorded by crossref.

1. Data and features enhancement for point clouds semantic segmentation of indoor building structures J. Zhou et al. https://doi.org/10.1088/2631-8695/ae72f0
2. Semantic enrichment for BIM: Enabling technologies and applications S. Jiang et al. https://doi.org/10.1016/j.aei.2023.101961
3. A Map Creation for LiDAR Localization Based on the Design Drawings and Tablet Scan Data S. Ito et al. https://doi.org/10.20965/jrm.2023.p0470
4. Automating the retrospective generation of As-is BIM models using machine learning P. Schönfelder et al. https://doi.org/10.1016/j.autcon.2023.104937
5. 3D Point Cloud Semantic Segmentation based on Multi-scale Dense Nested Networks Z. Wang et al. https://doi.org/10.54097/wx687k80
6. Scanning Technologies to Building Information Modelling: A Review R. Rashdi et al. https://doi.org/10.3390/infrastructures7040049
7. From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds F. Xue et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000839
8. Semantic Segmentation of Coastal Zone on Airborne Lidar Bathymetry Point Clouds S. Roshandel et al. https://doi.org/10.1109/LGRS.2022.3161191
9. Dynamically loading IFC models on a web browser based on spatial semantic partitioning H. Lu et al. https://doi.org/10.1186/s42492-019-0011-z
10. Indoor 3D wireframe construction from incomplete point clouds based on Gestalt rules Z. Qin et al. https://doi.org/10.1016/j.jag.2024.103893
11. Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities L. Rampini & F. Cecconi https://doi.org/10.36680/j.itcon.2022.043