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Segmentation of Structural Elements from 3D Point Cloud Using Spatial Dependencies for Sustainability Studies.

Joram Ntiyakunze¹, Tomo Inoue¹

¹Department of Environmental and Heritage Design, Graduate School of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka 815-8540, Japan.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study presents an unsupervised method for segmenting existing building point clouds, overcoming occlusion challenges to identify structural elements like walls and beams for better building analysis.

Keywords:

classification occlusion planar patches point cloud segmentation structural elements

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Area of Science:

Geomatics Engineering
Computer Vision
Structural Engineering

Background:

Accurate structural analysis of existing buildings requires detailed 3D models, often derived from point cloud data.
Occluding objects in point clouds of existing buildings present a significant challenge for automated segmentation.
Existing segmentation methods struggle with incomplete data and complex structural elements.

Purpose of the Study:

To develop an unsupervised point cloud segmentation method for existing buildings that addresses occlusion issues.
To accurately classify structural elements such as floor slabs, beams, walls, and columns.
To evaluate the proposed method's performance against state-of-the-art techniques.

Main Methods:

Developed a novel approach to connect disconnected planar patches caused by occlusions.
Integrated knowledge of building structures and spatial dependencies for segmentation.
Classified refined planar segments into common structural components (slabs, beams, walls, columns).

Main Results:

The unsupervised method successfully segmented structural elements from point clouds with high occlusion levels.
The approach demonstrated good results in segmenting structural elements by their constituent surfaces.
Performance was evaluated on a large dataset and compared favorably to recent segmentation methods.

Conclusions:

The proposed method offers an effective solution for segmenting occluded point clouds of existing buildings.
Further research is recommended to improve the segmentation accuracy for walls and beams.
The approach facilitates detailed structural analysis and life-cycle assessment of buildings.