Manfred Klopschitz

PhD Thesis

Robust Reconstruction and Efficient Localization for Mobile Augmented Reality, May 2012 thesis (PDF)

Research Interests

Short CV

Manfred Klopschitz 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)
Simultaneous localization and mapping (SLAM)
Augmented reality (AR)
Image-based localization

Click here for a list of publications.

Projects

AR Tracking

Automatic Reconstruction of Wide-Area Fiducial Marker Models

We present an approach towards automatic reconstruction of large assemblies of fiducial markers scattered throughout a wide indoor area, using a computer vision based reconstruction approach. The data is acquired from a video stream captured with a monoscopic camera. The system is capable of creating markers models that are significantly larger in physical area and number of markers than with previous approaches.

M. Klopschitz, D. Schmalstieg. ISMAR 2007. Preprint (PDF)

Towards Wide Area Localization on Mobile Phones

We present a fast and memory effcient method for localizing a mobile user's 6DOF pose from a single camera image. Our approach registers a view with respect to a sparse 3D point reconstruction. The 3D point dataset is partioned into pieces based on visibility constraints and occlusion culling, making it scalable and efficient to handle. Starting with a coarse guess, our system only considers features that can be seen from the user's position. Our method is resource efficient, usually requiring only a few megabytes of memory, thereby making it feasible to run on low-end devices such as mobile phones. At the same time it is fast enough to give instant results on this device class.

Wide Area Localization on Mobile Phones;Arth Clemens, Wagner Daniel, Klopschitz Manfred, Irschara Arnold , Schmalstieg Dieter; ISMAR 2009 Preprint (PDF)

Structure from Motion

Generalized Detection and Merging of Loop Closures for Video Sequences

In this work we present a method to detect overlaps in image sequences, and use this information to integrate overlapping sparse 3D structure from video sequences. The additional temporal information of these images is used to increase robustness over single image pair matching. A scanline optimization problem formulation is used to compute the best sequence alignment using wide-baseline image matching techniques. Compared to a direct dynamic programming approach, the scanline optimization formulation increases the robustness of sequence alignment for general relative motions. The proposed alignment method is employed to integrate sparse 3D models reconstructed from separate video sequences. In addition loop closures are detected. Consequently, the 3D modeling process from sequential image data can be split into fast sequence processing and subsequent global integration steps.