Robust wide baseline scene alignment based on 3D viewpoint normalization

Michael Ying Yang, Yanpeng Cao, Wolfgang Förstner, John McDonald

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Visual Computing : 6th International Symposium, ISVC 2010, Proceedings
EditorsG. Bebis, R. Boyle, B. Parvin
PublisherSpringler Verlag
Number of pages12
EditionPART 1
ISBN (Print)3642172881, 9783642172885
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event6th International, Symposium on Visual Computing, ISVC 2010 - Las Vegas, United States
Duration: 29 Nov 20101 Dec 2010
Conference number: 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6453 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International, Symposium on Visual Computing, ISVC 2010
Abbreviated titleISVC 2010
Country/TerritoryUnited States
CityLas Vegas


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