Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification

Changqin Huang

doi:10.1109/TNNLS.2022.3162301

Return

Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification

2024-04-01

PRE

黄

黄昌勤 (Changqin Huang) *

DOI：10.1109/TNNLS.2022.3162301

Original

Original request for help

Save

Abstract

En 中文

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Keywords:

Point cloud compression

Convolution

Feature extraction

Shape

Deep learning

Convolutional neural networks

Aggregates

3-D point cloud

geometric attention mechanism

graph convolution networks

intrinsic and extrinsic features