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

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 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

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

scholarly journals Point cloud classification by dynamic graph CNN with adaptive feature fusion

2021 ◽  
Vol 15 (3) ◽  
pp. 235-244
Author(s):  
Rui Guo ◽  
Yong Zhou ◽  
Jiaqi Zhao ◽  
Yiyun Man ◽  
Minjie Liu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Feature Fusion ◽  
Dynamic Graph ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2846 ◽  
Author(s):  
Tong ◽  
Li ◽  
Zhang ◽  
Chen ◽  
Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Single Point ◽  
Point Clouds ◽  
Max Pooling ◽  
Multi Scale ◽  
Cloud Classification ◽  
Point Set ◽  
Multi Level ◽  
Point Cloud Classification

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

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