10 April 2018 LSAH: a fast and efficient local surface feature for point cloud registration
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151G (2018) https://doi.org/10.1117/12.2303809
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.
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Rongrong Lu, Rongrong Lu, Feng Zhu, Feng Zhu, Qingxiao Wu, Qingxiao Wu, Yanzi Kong, Yanzi Kong, } "LSAH: a fast and efficient local surface feature for point cloud registration", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151G (10 April 2018); doi: 10.1117/12.2303809; https://doi.org/10.1117/12.2303809
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