Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

Poux, Florent; Billen, Roland

doi:10.3390/ijgi8050213

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Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

Florent Poux

and

Roland Billen

Geomatics Unit, University of Liège (ULiege), Allée du six Août, 19, 4000 Liège, Belgium

Author to whom correspondence should be addressed.

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)

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Abstract

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

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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 Style

Poux, Florent, and Roland Billen. 2019. "Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods" ISPRS International Journal of Geo-Information 8, no. 5: 213. https://doi.org/10.3390/ijgi8050213

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Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

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