scholarly journals A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

2020 ◽  
Vol 12 (16) ◽  
pp. 2598
Author(s):  
Simone Teruggi ◽  
Eleonora Grilli ◽  
Michele Russo ◽  
Francesco Fassi ◽  
Fabio Remondino
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Large Scale ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Cloud Classification ◽  
3D Data ◽  
Structured Information ◽  
Multi Level ◽  
Point Cloud Classification

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

scholarly journals Point‐selection and multi‐level‐point‐feature fusion‐based 3D point cloud classification

2020 ◽  
Vol 56 (6) ◽  
pp. 290-293
Author(s):  
Qieshi Zhang ◽  
Jun Cheng ◽  
Shengwen Wang ◽  
Chengjun Xu ◽  
Xiangyang Gao
Keyword(s):  
Point Cloud ◽  
Feature Fusion ◽  
3D Point Cloud ◽  
Point Selection ◽  
Cloud Classification ◽  
Multi Level ◽  
Point Cloud Classification ◽  
Point Feature

An adaptive locally-coded point cloud classification and segmentation network coupled with genetic algorithm

2021 ◽  
pp. 1-16
Author(s):  
Ma Qihang ◽  
Zh Jian ◽  
Zhang Jiahao
Keyword(s):  
Genetic Algorithm ◽  
Point Cloud ◽  
Cloud Data ◽  
Fine Grained ◽  
Network Training ◽  
Cloud Classification ◽  
3D Data ◽  
Average Accuracy ◽  
Mutation Strategy ◽  
Point Cloud Classification

Local information coding helps capture the fine-grained features of the point cloud. The point cloud coding mechanism should be applicable to the point cloud data in different formats. However, the local features of the point cloud are directly affected by the attributes, size and scale of the object. This paper proposes an Adaptive Locally-Coded point cloud classification and segmentation Network coupled with Genetic Algorithm(ALCN-GA), which can automatically adjust the size of search cube to complete network training. ALCN-GA can adapt to the features of 3D data at different points, whose adjustment mechanism is realized by designing a robust crossover and mutation strategy. The proposed method is tested on the ModelNet40 dataset and S3DIS dataset. Respectively, the overall accuracy and average accuracy is 89.5% and 86.5% in classification, and overall accuracy and mIoU of segmentation is 80.34% and 51.05%. Compared with PointNet, average accuracy in classification and mIoU of segmentation is improved about 10% and 11% severally.

scholarly journals GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 157-164 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi ◽  
C. Mallet ◽  
M. Weinmann
Keyword(s):  
Influencing Factors ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Sampling ◽  
Cloud Data ◽  
Semantic Classes ◽  
Automatic Interpretation ◽  
Point Cloud Classification ◽  
The Impact

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (<i>linear structures, planar structures</i> and <i>volumetric structures</i>) as well as a reference labeling with respect to five semantic classes (<i>Wire, Pole/Trunk, Façade, Ground</i> and <i>Vegetation</i>) is available.

scholarly journals Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification

2021 ◽  
pp. 573-581
Author(s):  
Sylvain Chabanet ◽  
Valentin Chazelle ◽  
Philippe Thomas ◽  
Hind Bril El-Haouzi
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Point Cloud Classification
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