Share Email Print
cover

Proceedings Paper • new

Efficient 3D correspondence grouping by two-stage filtering
Author(s): Rongrong Lu; Feng Zhu; Qingxiao Wu; Yanzi Kong
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents an efficient 3D correspondence grouping algorithm for finding inliers from an initial set of feature matches. The novelty of our approach lies in the use of a combination of pair-wise and triple-wise geometric constraints to filter the outliers from the initial correspondence. The triple-wise geometric constraint is built by considering three pairs of corresponding points simultaneously. A global reference point generated according to the model shape can be mapped to the scene shape thereby form a derived point by the triple-wise geometric constraint. Then, all the initial correspondence can be filtered once via the global reference point and the derived point by using some simple and low-level geometric constraints. Afterwards, the remaining correspondences will be further filtered by means of the pair-wise geometric consistency algorithm. Finally, more accurate matching results can be obtained. The experimental results show the superior performance of our approach with respect to the noise, point density variation and partial overlap. Our algorithm strikes a good balance between accuracy and speed.

Paper Details

Date Published: 6 May 2019
PDF: 9 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690Z (6 May 2019); doi: 10.1117/12.2524157
Show Author Affiliations
Rongrong Lu, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Feng Zhu, Shenyang Institute of Automation (China)
Key Lab. of Opto-Electronic Information Processing (China)
Key Lab. of Image Understanding and Computer Vision (China)
Qingxiao Wu, Shenyang Institute of Automation (China)
Key Lab. of Opto-Electronic Information Processing (China)
Key Lab. of Image Understanding and Computer Vision (China)
Yanzi Kong, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

© SPIE. Terms of Use
Back to Top