Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods
Geomatics Unit, University of Liège (ULiege), Allée du six Août, 19, 4000 Liège, Belgium
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ISPRS Int. J. Geo-Inf. 2019, 8(5), 213; https://doi.org/10.3390/ijgi8050213
Received: 6 March 2019 / Revised: 11 April 2019 / Accepted: 3 May 2019 / Published: 7 May 2019
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning.
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Keywords:
3D point cloud; voxel; feature extraction; semantic segmentation; classification; 3D semantics; deep learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Poux, F.; Billen, R. Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods. ISPRS Int. J. Geo-Inf. 2019, 8, 213. https://doi.org/10.3390/ijgi8050213
AMA Style
Poux F, Billen R. Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods. ISPRS International Journal of Geo-Information. 2019; 8(5):213. https://doi.org/10.3390/ijgi8050213
Chicago/Turabian StylePoux, Florent; Billen, Roland. 2019. "Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods" ISPRS Int. J. Geo-Inf. 8, no. 5: 213. https://doi.org/10.3390/ijgi8050213
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