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Open AccessArticle

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
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. View Full-Text
Keywords: 3D point cloud; voxel; feature extraction; semantic segmentation; classification; 3D semantics; deep learning 3D point cloud; voxel; feature extraction; semantic segmentation; classification; 3D semantics; deep learning
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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.

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