Large Scale Multi-view Stereopsis Evaluation

Rasmus Ramsbøl Jensen, Anders Bjorholm Dahl, George Vogiatzis, Engin Tola, Henrik Aanæs

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.
Original languageEnglish
Title of host publication2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publication date2014
ISBN (Print)9781479951185
Publication statusPublished - 2014
Event2014 IEEE Conference on Computer Vision and Pattern Recognition - Columbus, United States
Duration: 23 Jun 201428 Jun 2014


Conference2014 IEEE Conference on Computer Vision and Pattern Recognition
Country/TerritoryUnited States
Internet address
SeriesI E E E Conference on Computer Vision and Pattern Recognition. Proceedings


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