.png){kind=icon}
Return
Point cloud sampling method based on offset-attention and mutual supervision
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-022-02440-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In applications based on a three-dimensional point cloud, massive point cloud data often brings processing difficulties. To deal with the problem, many point cloud sampling methods were proposed. But there are still some issues in these methods: (i) lack the consideration of geometric features and (ii) how to train the distribution of projected points by the observed coefficient effectively. This paper introduces a fine-tuned pointnet module, which extracts the geometric features of points and applies the offset-attention mechanism to enhance the feature expression ability. Furthermore, it corrects the positions of simplified points by a mutual supervision loss. The experimental results show our method can improve the effectiveness and robustness of the point cloud sampling.
Keywords:
Point cloud sampling
Geometric features
Offset-attention mechanism
Mutual supervision
Journal
IF:
2.9
Papers:
4.6K
Citations:
6.5K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
View more