.png){kind=icon}
.png){kind=icon}
Return
TSKPD: twin structure key point detection in point cloud
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-024-01593-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The point cloud keypoint detection algorithm like USIP that uses downsampling first and then fine-tuning the sampling points cannot effectively detect the defect part of the single view defect point cloud, resulting in the inability to output the keypoints of the defect part. Therefore, this paper proposes the twin structure key point detection algorithm named TSKPD based on the idea of contrastive learning, which uses two single-view defect point clouds to synthesize relatively more complete key points for learning, so as to promote the network model to learn the features of the complete point cloud. The robustness of key point detection of point cloud is effectively improved, and the detection of single view defect point cloud is realized. The test results on ModelNet40 and ShapeNet datasets show that the coverage rate of TSKPD on the missing part of the single view defect point cloud is 12.62 higher than the existing optimal algorithm.
Keywords:
Point cloud keypoint detection
Contrastive learning
Unsupervised learning
Deep Learning
Journal
IF:
4.6
Papers:
2.1K
Citations:
6.6K