Publications by Engin Tola | EPFL Graph Search

Related publications (6)

DAISY: A Fast Descriptor for Dense Wide Baseline Stereo and Multiview Reconstruction

Stereo 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 w ...

EPFL2010

A fast local descriptor for dense matching

Pascal Fua, Vincent Lepetit, Engin Tola

We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used corre ...

2008

Efficient Large Scale Multi-View Stereo for Ultra High Resolution Image Sets

Pascal Fua, Christoph Strecha, Engin Tola

We present a new approach for large scale multi-view stereo matching, which is designed to operate on ultra high resolution image sets and efficiently compute dense 3D point clouds. We show that, by using a robust descriptor for matching purposes and high ...

Springer Verlag2012

Template-Free Monocular Reconstruction of Deformable Surfaces

Pascal Fua, Mathieu Salzmann, Engin Tola, Aydin Varol

It has recently been shown that deformable 3D surfaces could be recovered from single video streams. However, existing techniques either require a reference view in which the shape of the surface is known a priori, which often may not be available, or requ ...

2009

Large occlusion completion using normal maps

Pascal Fua, Christoph Strecha, Engin Tola

Dense disparity maps can be computed from wide-baseline stereo pairs but will inevitably contain large areas where the depth cannot be estimated accurately because the pixels are seen in one view only. A traditional approach to this problem is to introduce ...

2010

Daisy: An Efficient Dense Descriptor Applied to Wide Baseline Stereo

Pascal Fua, Vincent Lepetit, Engin Tola

In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM based algorithm to compute dense depth and occlusion maps from wide baseline image pairs using this descriptor. This yields much ...

Institute of Electrical and Electronics Engineers2010