Summary

Image description This research work tackles the problem of dense three-dimensional reconstruction from monocular image sequences. Recovering 3D-information has been in the focus of attention of the computer vision community for a few decades now, yet no all-satisfying method has been found so far. The main problem with vision, is that the perceived computer image is a two-dimensional projection of the 3D world. Three-dimensional reconstruction can thus be regarded as the process of re-projecting the 2D image(s) back to a 3D model, as such recovering the depth dimension which was lost during projection.

In this work, we focus on dense reconstruction, meaning that a depth estimate is sought for each pixel of the input image. Most attention in the 3Dreconstruction area has been on stereo-vision based methods, which use the displacement of objects in two (or more) images. Where stereo vision must be seen as a spatial integration of multiple viewpoints to recover depth, it is also possible to perform a temporal integration. The problem arising in this situation is known as the Structure from Motion problem and deals with extracting 3- dimensional information about the environment from the motion of its projection onto a two-dimensional surface. The data fusion problem arising in this case is solved by casting it as an energy minimization problem in a variational framework.