AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation

Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. There...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (4), p.1036
Main Authors: Jing, Weipeng, Zhang, Wenjun, Li, Linhui, Di, Donglin, Chen, Guangsheng, Wang, Jian
Format: Article
Language:English
Subjects:
3D point clouds
Accuracy
Aggregates
Classification
Deep learning
Experiments
Feature extraction
geometric features
graph attention mechanism
Image segmentation
Information processing
Methods
neural network
Neural networks
Performance evaluation
Remote sensing
Semantics
shape analysis
Spatial data
Topology
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Summary:Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14041036