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.

像USIP这样先下采样再微调采样点的点云关键点检测算法无法有效检测单视缺陷点云的缺陷部分，导致无法输出缺陷部分的关键点。因此，本文提出基于对比学习思想的孪生结构关键点检测算法TSKPD，利用两个单视缺陷点云合成相对更完整的关键点进行学习，从而促进网络模型学习完整点云的特征。有效提高了点云关键点检测的鲁棒性，实现了单视点缺陷点云的检测。在ModelNet40和ShapeNet数据集上的测试结果表明，TSKPD对单视缺陷点云缺失部分的覆盖率比现有最优算法高12.62。