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

ISPRS International Journal of Geo-Information

Florent Poux¹  Roland Billen¹ 

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

关键词: 3D point cloud;    voxel;    feature extraction;    semantic segmentation;    classification;    3D semantics;    deep learning;   

DOI  :  10.3390/ijgi8050213

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown