http://hdl.handle.net/10900/53326 2026-05-12T12:19:06Z 2026-05-12T12:19:06Z Huhle, Benjamin Schairer, Timo Herholz, Sebastian Schilling, Andreas Straßer, Wolfgang http://hdl.handle.net/10900/49748 2019-10-30T03:33:28Z 2012-01-01T00:00:00Z

Sparse Registration - 3D Reconstruction from Pairs of 2D Line Scans; Sparse Registration - 3D Reconstruction from Pairs of 2D Line Scans Huhle, Benjamin; Schairer, Timo; Herholz, Sebastian; Schilling, Andreas; Straßer, Wolfgang We address a new registration problem: Using a coupled pair of 2d scanners, we capture range data by freely moving the system through the scene. The registration with regard to six degrees of freedom becomes solvable due to the fact that rst, the pair of line scanners has dierent orientation, and second, we use a volume-oriented algorithm instead of commonly used surface-oriented approaches. We present a method that is based on the idea of preserving the free space represented in each of the scans. The proposed algorithm is evaluated with real range data associated with orientation estimates from an inertia sensor. Additionally, we provide quantitative results with simulated data. In both cases, the algorithm is capable to recover from large translational and moderate rotational errors in the initial conguration.

2012-01-01T00:00:00Z Ajdin, Boris Finckh, Manuel Fuchs, Christian Hanika, Johannes Lensch, Hendrik P. A. http://hdl.handle.net/10900/44129 2019-10-30T04:51:57Z 2012-01-01T00:00:00Z

Compressive Higher-order Sparse and Low-Rank Acquisition with a Hyperspectral Light Stage Ajdin, Boris; Finckh, Manuel; Fuchs, Christian; Hanika, Johannes; Lensch, Hendrik P. A. Compressive sparse and low-rank recovery (CSLR) is a novel method for compressed sensing deriving a low-rank and a sparse data terms from randomized projection measurements. While previous approaches either applied compressive measurements to phenomena assumed to be sparse or explicitly assume and measure low-rank approximations, CSLR is inherently robust if any such assumption might be violated. In this paper, we will derive CSLR using Fixed-Point Continuation algorithms, and extend this approach in order to exploit the correlation in high-order dimensions to further reduce the number of captured samples. Though generally applicable, we demonstrate the effectiveness of our approach on data sets captured with a novel hyperspectral light stage that can emit a distinct spectrum from each of the 196 light source directions enabling bispectral measurements of reflectance from arbitrary viewpoints. Bispectral reflectance fields and BTFs are faithfully reconstructed from a small number of compressed measurements.

2012-01-01T00:00:00Z Effinger, Philip Kaufmann, Michael Meinert, Sascha Stegmaier, Matthias http://hdl.handle.net/10900/49561 2019-10-30T03:07:10Z 2011-01-01T00:00:00Z

GraphArchive - An Online Graph Data Store Effinger, Philip; Kaufmann, Michael; Meinert, Sascha; Stegmaier, Matthias In this report, we present our approach 'GraphArchive'. The solution attempts to enable researchers to exchange and archive graphs. The software is developed as an online platform using modern web technologies. In the document, features and architecture of GraphArchive are presented and the former approach 'GraphDB' is compared to the new system. Also, reader are taken on a typical walk through the system using a common use case for GraphArchive. News and development status of the system can be also visited at http://www.graph-archive.org.

2011-01-01T00:00:00Z Kottler, Stephan Kaufmann, Michael http://hdl.handle.net/10900/49522 2019-10-30T03:07:11Z 2011-01-01T00:00:00Z

A parallel portfolio SAT solver with lockless physical clause sharing Kottler, Stephan; Kaufmann, Michael Since multi-core architectures have become well-established the enquiry for parallel SAT solvers has drastically increased. Meanwhile, several successful SAT solvers have been presented that can be run in parallel mode. However, there are only a few solvers that use the shared memory architectures for physical clause sharing. In this paper we present a parallel SAT solver that allows for sharing clauses between several threads logically and physically. Yet any thread is still able to keep its own set of clauses. We show how physical clause sharing can be used to propagate one thread's improvements on the clause database to all solving threads. Despite the extensive sharing of data our solver does not require any operating system lock.

2011-01-01T00:00:00Z