Maarten Vergauwen

Bio: Maarten Vergauwen is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Point cloud & Computer science. The author has an hindex of 20, co-authored 112 publications receiving 3003 citations. Previous affiliations of Maarten Vergauwen include Catholic University of Leuven & ETH Zurich.

Visual Modeling with a Hand-Held Camera

Abstract: In this paper a complete system to build visual models from camera images is presented. The system can deal with uncalibrated image sequences acquired with a hand-held camera. Based on tracked or matched features the relations between multiple views are computed. From this both the structure of the scene and the motion of the camera are retrieved. The ambiguity on the reconstruction is restricted from projective to metric through self-calibration. A flexible multi-view stereo matching scheme is used to obtain a dense estimation of the surface geometry. From the computed data different types of visual models are constructed. Besides the traditional geometry- and image-based approaches, a combined approach with view-dependent geometry and texture is presented. As an application fusion of real and virtual scenes is also shown.

Web-based 3D Reconstruction Service

Abstract: The use of 3D information in the field of cultural heritage is increasing year by year. From this field comes a large demand for cheaper and more flexible ways of 3D reconstruction. This paper describes a web-based 3D reconstruction service, developed to relieve those needs of the cultural heritage field. This service consists of a pipeline that starts with the user uploading images of an object or scene(s) he wants to reconstruct in 3D. The automatic reconstruction process, running on a server connected to a cluster of computers, computes the camera calibration, as well as dense depth (or range-) maps for the images. This result can be downloaded from an ftp server and visualized with a specific tool running on the user’s PC.

Automated reconstruction of 3D scenes from sequences of images

Abstract: Modelling of 3D objects from image sequences is a challenging problem and has been an important research topic in the areas of photogrammetry and computer vision for many years. In this paper, a system is presented which automatically extracts a textured 3D surface model from a sequence of images of a scene. The system can deal with unknown camera settings. In addition, the parameters of this camera are allowed to change during acquisition (e.g., by zooming or focusing). No prior knowledge about the scene is necessary to build the 3D models. Therefore, this system offers a high degree of flexibility. The system is based on state-of-the-art algorithms recently developed in computer vision. The 3D modelling task is decomposed into a number of successive steps. Gradually, more knowledge of the scene and the camera setup is retrieved. At this point, the obtained accuracy is not yet at the level required for most metrology applications, but the visual quality is very convincing. This system has been applied to a number of applications in archaeology. The Roman site of Sagalassos (southwest Turkey) was used as a test case to illustrate the potential of this new approach.

A Hierarchical Symmetric Stereo Algorithm Using Dynamic Programming

Abstract: In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are downsampled an optimal number of times and the disparity map of a lower level is used as ‘offset’ disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparityrange. This result is also used to determine the optimal number of downsample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. The algorithm in this paper is symmetric, i.e. exactly the same matches are found if left and right image are swapped. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.

3D Reconstruction from Multiple Images Part 1: Principles

Abstract: This issue discusses methods to extract three-dimensional (3D) models from plain images. In particular, the 3D information is obtained from images for which the camera parameters are unknown. The principles underlying such uncalibrated structure-from-motion methods are outlined. First, a short review of 3D acquisition technologies puts such methods in a wider context and highlights their important advantages. Then, the actual theory behind this line of research is given. The authors have tried to keep the text maximally self-contained, therefore also avoiding to rely on an extensive knowledge of the projective concepts that usually appear in texts about self-calibration 3D methods. Rather, mathematical explanations that are more amenable to intuition are given. The explanation of the theory includes the stratification of reconstructions obtained from image pairs as well as metric reconstruction on the basis of more than two images combined with some additional knowledge about the cameras used. Readers who want to obtain more practical information about how to implement such uncalibrated structure-from-motion pipelines may be interested in two more Foundations and Trends issues written by the same authors. Together with this issue they can be read as a single tutorial on the subject.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Computer Vision: Algorithms and Applications

Abstract: Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Photo tourism: exploring photo collections in 3D

Abstract: We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Stereo Processing by Semiglobal Matching and Mutual Information

Abstract: This paper describes the semiglobal matching (SGM) stereo method. It uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement, and multibaseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments, and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed. A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2 seconds on typical test images. An in depth evaluation of the Ml-based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.