Yang, Michael Ying and Cao, Yanpeng and Förstner, Wolfgang and McDonald, John (2010) Robust Wide Baseline Scene Alignment Based on 3D Viewpoint Normalization. In: ISVC 2010: Advances in Visual Computing. Lecture Notes in Computer Science book series (LNCS) (6543). Springer, pp. 654-665. ISBN 9783642172885

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Abstract

This paper presents a novel scheme for automatically aligning two widely separated 3D scenes via the use of viewpoint invariant features. The key idea of the proposed method is following. First, a number of dominant planes are extracted in the SfM 3D point cloud using a novel method integrating RANSAC and MDL to describe the underlying 3D geometry in urban settings. With respect to the extracted 3D planes, the original camera viewing directions are rectified to form the front-parallel views of the scene. Viewpoint invariant features are extracted on the canonical views to provide a basis for further matching. Compared to the conventional 2D feature detectors (e.g. SIFT, MSER), the resulting features have following advantages: (1) they are very discriminative and robust to perspective distortions and viewpoint changes due to exploiting scene structure; (2) the features contain useful local patch information which allow for efficient feature matching. Using the novel viewpoint invariant features, wide-baseline 3D scenes are automatically aligned in terms of robust image matching. The performance of the proposed method is comprehensively evaluated in our experiments. It’s demonstrated that 2D image feature matching can be significantly improved by considering 3D scene structure.

Item Type:	Book Section
Additional Information:	Cite this paper as: Yang M.Y., Cao Y., Förstner W., McDonald J. (2010) Robust Wide Baseline Scene Alignment Based on 3D Viewpoint Normalization. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg
Keywords:	Scene Alignment; 3D Viewpoint Normalization; viewpoint invariant features;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8257
Identification Number:	https://doi.org/10.1007/978-3-642-17289-2_63
Depositing User:	John McDonald
Date Deposited:	30 May 2017 14:44
Publisher:	Springer
Refereed:	Yes
URI:	https://link.springer.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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