Kpsnet: Keypoint Detection and Feature Extraction for Point Cloud Registration

Publication Type:
Conference Proceeding
Citation:
Proceedings - International Conference on Image Processing, ICIP, 2019, 2019-September pp. 2576 - 2580
Issue Date:
2019-09-01
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10.1109icip.2019.88033 am.pdfAccepted Manuscript Version610.95 kB
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© 2019 IEEE. This paper presents the KPSNet, a KeyPoint Siamese Network to simultaneously learn task-desirable keypoint detector and feature extractor. The keypoint detector is optimized to predict a score vector, which signifies the probability of each candidate being a keypoint. The feature extractor is optimized to learn robust features of keypoints by exploiting the correspondence between the keypoints generated from two inputs, respectively. For training, the KPSNet does not require to manually annotate keypoints and local patches pairwise. Instead, we design an alignment module to establish the correspondence between the two inputs and generate positive and negative samples on-the-fly. Therefore, our method can be easily extended to new scenes. We test the proposed method on the open-source benchmark and experiments show the validity of our method.
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