Earticle

초록

영어

Single-view reconstruction (SVR) is a fundamental method in computer vision. Often used for reconstructing human-made environments, the Manhattan world assumption presumes that planes in the real world exist in mutually orthogonal directions. Accordingly, this paper addresses an automatic SVR algorithm for Manhattan worlds. A method for estimating the directions of planes using graph-cut optimization is proposed. After segmenting an image from extracted line segments, the data cost function and smoothness cost function for graph-cut optimization are defined by considering the directions of the line segments and neighborhood segments. Furthermore, segments with the same depths are grouped during a depth-estimation step using a minimum spanning tree algorithm with the proposed weights. Experimental results demonstrate that, unlike previous methods, the proposed method can identify complex Manhattan structures of indoor and outdoor scenes and provide the exact boundaries and intersections of planes.

목차

Abstract
1. Introduction
2. Related work
3. Proposed method
3.1 Pre-processing
3.2 Vanishing point detection and finding Manhattan directions
3.3 Segment normal estimation
3.4 Segment depth estimation
4. Experimental results
4.1 Normal estimation results
4.2 3D reconstruction results
5. Conclusions
References

키워드

저자

• Suwon Lee [ Associate Professor, School of Computer Science and the Research Institute of Natural Science, Gyeongsang National University, Korea ]
• Yong-Ho Seo [ Professor, Department of AI and Robot Convergence, Mokwon University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380