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Performance Evaluating of some Methods in 3D Depth Reconstruction from a Single Image
Blekinge Institute of Technology, School of Engineering.
2009 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

We studied the problem of 3D reconstruction from a single image. The 3D reconstruction is one of the basic problems in Computer Vision. The 3D reconstruction is usually achieved by using two or multiple images of a scene. However recent researches in Computer Vision field have enabled us to recover the 3D information even from only one single image. The methods used in such reconstructions are based on depth information, projection geometry, image content, human psychology and so on. Each method has certain advantages and can be used to recover the 3D information from certain types of images according to their contents. There is not a standard evaluation method which can compare such these methods. In the thesis five methods of 3D reconstruction of single images are chosen. We review the methods theoretically and compare their 3D results. The five methods are Make3D, Automatic photo pop-up, Auto3D, Metric Rectification and Psychological Stereo which are representative from different types of methods of 3D reconstruction from a single image. Two different evaluation methods were implemented in the thesis. One is based on human inquires and how they experience the 3D reconstruction result of each method and the other one is based on a novel objective method where a controlled scene in form of its complexity was used. The performance of the methods in 3D information reconstruction is reported. Our novel evaluation method shows the benefit of objectivity and reliability of method which can be implemented easy in such difficult comparison situations.

Place, publisher, year, edition, pages
2009. , 54 p.
Keyword [en]
3D reconstruction, single image, depth, superpixel, stereo images, psychological stereoscopy, vanishing point, vanishing line.
National Category
Signal Processing
URN: urn:nbn:se:bth-2323Local ID: diva2:829592
Available from: 2015-04-22 Created: 2009-12-18 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

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