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3D Line Segment Reconstruction in Structured Scenes via Coplanar Line Segment Clustering

  • Kai Li
  • Jian YaoEmail author
  • Li Li
  • Yahui Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10117)

Abstract

This paper presents a new algorithm aiming for 3D Line Segment (LS) reconstruction in structured scenes that are comprised of a set of planes. Due to location imprecision of image LSs, it often produces many erroneous reconstructions when reconstructing 3D LSs by triangulating corresponding LSs from two images. We propose to solve this problem by first recovering space planes and then back-projecting image LSs onto the recovered space planes to get reliable 3D LSs. Given LS matches identified from two images, we estimate a set of planar homographies and use them to cluster the LS matches into groups such that LS matches in each group are related by the same homography induced by a space plane. In each LS match group, the corresponding space plane can be recovered from the 3D LSs obtained by triangulating all the LS correspondences. To reduce the incidence of incorrect LS match grouping, we formulate to solve the LS match grouping problem into solving a multi-label optimization problem. The advantages of the proposed algorithm over others in this area are that it can generate more complete and detailed 3D models of scenes using much fewer images and can recover the space planes where the reconstructed 3D LSs lie, which is beneficial for upper level applications, like scene understanding and building facade extraction.

Keywords

Line Segment Point Match Space Plane Reconstruction Accuracy Adjacency Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (Project No. 41571436), the National Natural Science Foundation of China under Grant 91438203, the Hubei Province Science and Technology Support Program, China (Project No. 2015BAA027), the Jiangsu Province Science and Technology Support Program, China (Project No. BE2014866), and the South Wisdom Valley Innovative Research Team Program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanPeople’s Republic of China

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