Work

From clustering to classification: An uncertainty analysis of a classification approach using a Gaussian mixture model

Year: 2025

Type: article

Abstract: This paper presents a systematic pipeline for the segmentation of LiDAR data. The LiDAR data is specifically acquired using the LiDAR sensor of a BLK2GO and stored in a PLY format. The presented pipeline uses common post‐processing steps for point cloud processing. First, a RANSAC algorithm is used to remove the indoor point cloud scene from the walls, using plane detection for dominant structures. Subsequently, the indoor scene is clustered by a DBSCAN algorithm. The clustered indoor scene objects are then briefly classified manually. After the manual step, the pipeline is using a Gaussian Mixture Model (GMM). The GMM classifies the objects based on their probabilistic distributions of the geometric dimension. Preliminary results indicate that this workflow enables an efficient object detection with a minimal manual effort. Furthermore, the paper investigates uncertainties of process steps and gives new approaches for dealing with the uncertainties. This pipeline aims to establish a practical approach for generating input data for Scan2BIM applications.

Source: ce/papers

DOI: 10.1002/cepa.3346

Author: Nils Soeren Krause

Institution: University of Rostock

Language: English

Topic: 3D Surveying and Cultural Heritage

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,140