Construction scene change detection based on octree structure optimization

Xinjing Liu; Xuming Ge

doi:10.1117/12.3049115

27 November 2024 Construction scene change detection based on octree structure optimization

Xinjing Liu, Xuming Ge

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 1340211 (2024) https://doi.org/10.1117/12.3049115
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Aiming at the problem of complex construction scene with complicated change situations and the difficulty of identifying and extracting effective change information, based on multi-temporal LiDAR point cloud data, an octree structure optimization-based change detection method for construction scene is proposed. First, the octree index is constructed for the construction point cloud data of a large scene; then, the object surface continuity and internal connectivity constraints are added to find the continuous change point cloud with planar features, and the change entity detection for the construction scene is designed; finally, the experimental validation is carried out based on the designed change detection method using the collected multi-temporal construction scene point cloud data. The results show that the change detection algorithm optimized by octree structure can accomplish the change detection requirements of multi-temporal construction scenes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinjing Liu and Xuming Ge "Construction scene change detection based on octree structure optimization", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 1340211 (27 November 2024); https://doi.org/10.1117/12.3049115

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KEYWORDS

Point clouds

Voxels

Bridges

Semantics

Data acquisition

Deep learning

Distance measurement

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