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In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations without the need for any ground truth training data.
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1 Year, 1000km: The Oxford RobotCar Dataset
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E. Rublee, V. Rabaud, K. Konolige, and G. Bradski · 2011
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A comparison of methods for non-rigid 3d shape retrieval
Z. Lian and A. A. Godil · 2012
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Performance evaluation of 3d keypoint detectors
F. Tombari, S. Salti, and L. Di Stefano · 2013
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Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors
H. Deng, T. Birdal, and S. Ilic · 2018
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Ppfnet: Global context aware local features for robust 3d point matching
H. Deng, T. Birdal, and S. Ilic · 2018
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The perfect match: 3d point cloud matching with smoothed densities
Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser · 2018
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So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
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Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition
M. A. Uy and G. H. Lee · 2018
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3dfeat-net: Weakly supervised local 3d features for point cloud registration
Z. J. Yew and G. H. Lee · 2018
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