.png){kind=icon}
Return
Point-attention Net: a graph attention convolution network for point cloudsegmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-022-03985-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Point cloud classification and segmentation is a crucial yet challenging step towards 3D scene understanding. In response to the fact that 3D point clouds cannot be directly applied to traditional 2D image convolutional neural networks and sparse point cloud features are difficult to capture, an improved segmentation network Point-Attention Net is proposed. In comparison with existing techniques, the proposed method models on point cloud directly, avoiding the extra computational and memory cost of converting point cloud to other forms. It also employs a novel proportional discrete dilation model for analyzing the features of a point cloud on different scales simultaneously. Additionally, the proposed method combines the graph attention convolution and adaptive weight assignment techniques, yielding better segmentation performance on the edge point cloud and more accurate analysis of the adjacency and spatial geometric distribution. Experimental results show that our algorithm achieves 64.56 % and 74.1 % IoU on average on S3DIS and Semantic3D datasets respectively, outperforming the state of the art.
Keywords:
Point cloud segmentation
Graph convolution neural network
Attention mechanism
Weight
Journal
IF:
3.5
Papers:
7.6K
Citations:
1.7W
Organization
No organization information available
Cited Papers
.png){kind=icon}
Air Quality and Thermal Comfort in an Office with Underfloor, Mixing and Displacement Ventilation
PREAI
ShareSaveShareSave
ShareSaveShareSave
View more