TSKPD: twin structure key point detection in point cloud

Autor: Yangyue Feng, Xiaokang Yang, Yong Li, Lijuan Zhang, Yan Lv, Jinfang Jin
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 10, Iss 6, Pp 8213-8231 (2024)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-024-01593-y
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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