A Fast Weighted Registration Method of 3D Point Cloud Based on Curvature Feature-Reference-Cited by-同舟云学术

A Fast Weighted Registration Method of 3D Point Cloud Based on Curvature Feature

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 3rd International Conference on Multimedia and Image Processing - ICMIP 2018
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Author:

Liu Bing¹,Gao Xuehai¹,Liu Houde¹,Wang Xueqian¹,Liang Bin¹

Affiliation:

1. Center of Intelligent Control and Telescience, Shenzhen Engineering Laboratory of Geometry Measurement Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

Publisher

ACM Press

Reference11 articles.

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2. Ying He. An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors 2017, 17.

3. Martin A. Fischler and Robert C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Graphics and Image Processing. 1981.

4. Wu, Y.; Wang, W.; Lu, K.; Wei, Y.; Chen, Z. A new method for registration of 3D point sets with low overlapping ratios. Procedia CIRP 2015, 27, 202--206.

5. Zhou, W.; Chen, G.; Du, S.; Li, F. An improved iterative closest point algorithm using clustering. Laser Optoelectron. Prog. 2016, 53, 1--7.

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