Short CV

Arnold Irschara is a research assistant at the Institute for Computer Graphics and Vision, Graz University of Technology. He received a MSc degree in Telematics from Graz University of Technology in 2006.

Research Interests

• Structure from Motion (SfM)
• Large scale 3D reconstruction
• Image-based localization
• Image retrieval and object recognition

Click here for a list of publications.

Projects

Image-based Localization

From Structure-from-Motion Point Clouds to Fast Location Recognition

Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.

A. Irschara, C. Zach, J.-M. Frahm, H. Bischof. CVPR 2009. Preprint (PDF), Video

Structure from Motion

What Can Missing Correspondences Tell Us About 3D Structure and Motion?

Practically all existing approaches to structure and motion computation use only positive image correspondences to verify the camera pose hypotheses. Incorrect epipolar geometries are solely detected by identifying outliers among the found correspondences. Ambigous patterns in the images are often incorrectly handled by these standard methods. In this work we propose two approaches to overcome such problems. First, we apply non-monotone reasoning on view triplets using a Bayesian formulation. In contrast to two-view epipolar geometry, image triplets allow the prediction of features in the third image. Absence of these features (i.e. missing correspondences) enables additional inference about the view triplet. Furthermore, we integrate these view triplet handling into an incremental procedure for structure and motion computation. Thus, our approach is able to refine the maintained 3D structure when additional image data is provided.

C. Zach, A. Irschara, H. Bischof. CVPR 2008. Paper (PDF)

Towards Wiki-based Dense City Modeling

This work reports on the advances and on the current status of a terrestrial city modeling approach, which uses images contributed by end-users as input. Hence, the Wiki principle well known from textual knowledge databases is transferred to the goal of incrementally building a virtual representation of the occupied habitat. In order to achieve this objective, many state-of-the-art computer vision methods must be applied and modified according to this task. We describe the utilized 3D vision methods and show initial results obtained from the current image database acquired by in-house participants.

A. Irschara, C. Zach, H. Bischof. VRML Workshop in conjunction with ICCV 2007. Paper (PDF)