000150471 001__ 150471
000150471 005__ 20181205220156.0
000150471 0247_ $$2doi$$a10.5075/epfl-thesis-4830
000150471 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis4830-1
000150471 02471 $$2nebis$$a6131191
000150471 037__ $$aTHESIS
000150471 041__ $$aeng
000150471 088__ $$a4830
000150471 245__ $$aDAISY: A Fast Descriptor for Dense Wide Baseline Stereo and Multiview Reconstruction
000150471 269__ $$a2010
000150471 260__ $$aLausanne$$bEPFL$$c2010
000150471 300__ $$a173
000150471 336__ $$aTheses
000150471 520__ $$aStereo reconstruction is a fundamental problem of computer       vision. It has been studied for more than three decades and       significant progress has been made in recent years as       evidenced by the quality of the models now being produced.       This is highly related with the advances in other fields.       With the emergence of low cost high-quality cameras, we now       live in an era where there is an abundant amount of data for       use in reconstruction. The multitude of images with numerous       sources of capture arose new interest in the stereo vision       community due to new challenges such as being robust to       photometric and geometric variability, scalability issues       related to number of images and image resolutions. In this thesis, we aim to find efficient, and       therefore practical, algorithmic solutions for the two       extreme ends of stereo vision problem: first, we consider       only two input image case where the cameras are placed far       from each other and then we investigate the large scale       multi-view reconstruction for ultra-high resolution image       sets. Both problems have unique challenges where in the first       part we need to handle the large perspective distortions that       the image texture undergoes and in the second part we need to       design an algorithm that can scale up to ultra-high       resolution very large number of image sets using only a       single standard computer. For the first problem, we design an efficient dense image       descriptor, called DAISY, that is not only robust to       photometric transforms like brightness and contrast changes       but also robust to perspective effects that view-point       changes produce. We use the DAISY descriptor as a       photo-consistency measure in an expectation maximization       framework with a global graph-cuts optimization algorithm to       estimate depth and occlusion maps. We demonstrate very       successful results on a variety of data sets some of which       have laser scanned ground truths. After the estimation of       depth and occlusion maps, we introduce a technique to improve       the surface reconstruction in occluded areas by extracting       normal cues using simple binary classifiers trained over       DAISY-like features. For the large scale ultra-high resolution multi-view       stereo problem, we design a very efficient local optimization       algorithm instead of the global one developed in the first       part of the thesis for the depth estimation framework. The       scalability over the number of images is handled by       representing the scene with a set of depth maps and the       scalability over the image resolution is handled by the use       of a local approach for depth map estimation. We demonstrate       state-of-the-art quality results for very large sets of very       high resolution images computed on a single standard computer       at comparatively very short computation times. Overall, we show that the use of a distinctive and robust       descriptor to measure photo-consistency allows us to avoid       many complex stages other algorithms utilize without       sacrificing from the accuracy of the results and thus scale       up to large data sets easily.
000150471 6531_ $$acomputer vision
000150471 6531_ $$adense local descriptor
000150471 6531_ $$aDAISY
000150471 6531_ $$ascene reconstruction
000150471 6531_ $$awide baseline stereo
000150471 6531_ $$aultra-high resolution
000150471 6531_ $$alarge scale multi-view stereo
000150471 6531_ $$avision par ordinateur
000150471 6531_ $$adescripteur local dense
000150471 6531_ $$aDAISY
000150471 6531_ $$areconstruction de scène
000150471 6531_ $$awide baseline stéréo
000150471 6531_ $$atrès haute résolution
000150471 6531_ $$amulti-vue stéréo à large échelle
000150471 700__ $$0242709$$aTola, Engin$$g170333
000150471 720_2 $$0240252$$aFua, Pascal$$edir.$$g112366
000150471 8564_ $$s147483271$$uhttps://infoscience.epfl.ch/record/150471/files/EPFL_TH4830.pdf$$yTexte intégral / Full text$$zTexte intégral / Full text
000150471 909C0 $$0252087$$pCVLAB$$xU10659
000150471 909CO $$ooai:infoscience.tind.io:150471$$pDOI$$pIC$$pthesis$$pthesis-bn2018$$qDOI2
000150471 918__ $$aIC$$cISIM$$dEDIC2005-2015
000150471 919__ $$aCVLAB
000150471 920__ $$b2010
000150471 970__ $$a4830/THESES
000150471 973__ $$aEPFL$$sPUBLISHED
000150471 980__ $$aTHESIS