Towards Building Abstraction by Using Line Segment Descriptor
Authors: 
Feng Guo, Ying Lin, Han Chen, Rongrong JiAuthors Info & Claims
Pages 136 - 141
Abstract
In order to obtain 3D information from unordered photo collections, traditional scene reconstruction methods are commonly based on feature points detection and matching, and Multi-view Stereo approaches are carried to refine the result due to the limitation of not having enough key points. But it usually gets an unideal effect when dealing with man-made environments such as buildings with low texture and repetitive parts. Considering the advantage of rich line segments of buildings, we propose a built abstraction approach which use the line segment descriptor and do the line segment matching using kd-tree, then develop an incremental approach to abstract buildings step by step. Comparing with feature based reconstruction methods which produce point clouds but lacking higher level building primitives, the experiments show that our method can generate sophisticated structure 3D model and facade model in a short time.
References
[1]
C. Akinlar and C. Topal. Edlines: A real-time line segment detector with a false detection control. Pattern Recognition Letters, 32(13):1633--1642, 2011.
[2]
J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509--517, 1975.
[3]
T. Chen and Q. Wang. 3d line segment detection for unorganized point clouds from multi-view stereo. In Computer Vision--ACCV 2010, pages 400--411. Springer, 2011.
[4]
S. Daftry, C. Hoppe, and H. Bischof. Building with drones: Accurate 3d facade reconstruction using mavs. CoRR, abs/1502.07019, 2015.
[5]
B. Fan, F. Wu, and Z. Hu. Line matching leveraged by point correspondences. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 390--397. IEEE, 2010.
[6]
R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.
[7]
G. Klein and D. Murray. Parallel tracking and mapping for small ar workspaces. In Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium on, pages 225--234. IEEE, 2007.
[8]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91--110, 2004.
[9]
S. M. S. N. Snavely and R. Szeliski. Modeling the world from internet photo collections. International Journal of Computer Vision (IJCV), 80(2):189--210, 2008.
[10]
M. Rothermel, K. Wenzel, D. Fritsch, and N. Haala. Sure: Photogrammetric surface reconstruction from imagery. In Proceedings LC3D Workshop, Berlin, pages 1--9, 2012.
[11]
S. N. Sinha, D. Steedly, R. Szeliski, M. Agrawala, and M. Pollefeys. Interactive 3d architectural modeling from unordered photo collections. In ACM Transactions on Graphics (TOG), volume 27, page 159. ACM, 2008.
[12]
N. Snavely et al. Bundler: Structure from motion (sfm) for unordered image collections. Available online: phototour. cs. washington. edu/bundler/(accessed on 12 July 2013), 2010.
[13]
N. Snavely, S. M. Seitz, and R. Szeliski. Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2):189--210, 2008.
[14]
C. J. Taylor and D. J. Kriegman. Structure and motion from line segments in multiple images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 17(11):1021--1032, 1995.
[15]
R. G. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall. Lsd: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis & Machine Intelligence, (4):722--732, 2008.
[16]
Z. Wang, F. Wu, and Z. Hu. Msld: A robust descriptor for line matching. Pattern Recognition, 42(5):941--953, 2009.
[17]
T. Werner and A. Zisserman. New techniques for automated architectural reconstruction from photographs. In Computer Visionï£ ¡ï£¡ECCV 2002, pages 541--555. Springer, 2002.
[18]
C. Wu. Visualsfm: A visual structure from motion system (2011). URL http://www.cs.washington.edu/homes/ccwu/vsfm.
[19]
L. Zhang and R. Koch. An efficient and robust line segment matching approach based on lbd descriptor and pairwise geometric consistency. Journal of Visual Communication and Image Representation, 24(7):794--805, 2013.
[20]
L. Zhang and R. Koch. Structure and motion from line correspondences: representation, projection, initialization and sparse bundle adjustment. Journal of Visual Communication and Image Representation, 25(5):904--915, 2014.
[21]
X. X. Zhu and M. Shahzad. Facade reconstruction using multiview spaceborne tomosar point clouds. Geoscience and Remote Sensing, IEEE Transactions on, 52(6):3541--3552, 2014.
- Towards Building Abstraction by Using Line Segment Descriptor
Recommendations
Generation of 3D Building Model Using 3D Line Segments
ICECT '09: Proceedings of the 2009 International Conference on Electronic Computer TechnologyThis paper presents a new method for building rooftop detection from aerial images. In our approach, we extract useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary ...
Extraction of 3D Line Segment Using Disparity Map
ICDIP '09: Proceedings of the International Conference on Digital Image Processing3D line segment can be regarded as one of the most useful features in constructing 3D model. In this context, this paper presents anew 3D line segment extraction method by using disparity map generated in the process of stereo matching. The core of our ...
Multimodal image matching based on Multimodality Robust Line Segment Descriptor
Although a number of local feature-based methods have been proposed, the multimodality matching is still a challenging problem in object recognition, remote sensing and medical image processing where the image contrast is significantly different. The ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
Copyright © 2016 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Xidian University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 August 2016
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
Acceptance Rates
ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 76Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in