Please check my Google Scholar profile for my publications.
Here you will find only a not complete and not up to date list of some selected papers:
An Approach to Describe Arbitrary Transition Curves in an IFC Based Alignment Product Data Model
Julian Amann¹, Matthias Flurl¹, Javier R. Jubierre¹, André Borrmann¹ , In: Proc. of the 2014 International Conference on Computing in Civil and Building Engineering, ISBN 978-0-7844-1361-6, Orlando, USA, 2014
Open standards for infrastructure based on IFC (Industry Foundation Classes) are mainly developed by the openINFRA initiative of the buildingSMART organization. Recently, several proposals for alignment models emerged with the development of the upcoming IFC 5 standard that in particular targets infrastructure projects such as roads, bridges and tunnel buildings. A common drawback of all these proposals is their limited description of arbitrary transition curves. For instance, in all proposed alignment models there are some missing types of transition curves, or different parameters are suggested to describe a certain transition curve type. Designing a neutral data format that satisfies all stakeholders in an international context is therefore difficult. A novel approach to describe transition curves based on the so-called IFCPL (Industry Foundation Classes Programming Language) is described and its integration into an IFC based alignment model is shown to avoid these problems.
Using Image Quality Assessment to Test Rendering Algorithms
Julian Amann¹, Bastian Weber¹, Charles A. Wüthrich¹, WSCG Full papers proceedings, ISBN 978-80-86943-74-9, pp. 205-214, Union Agency, 2013
Testing rendering algorithms is time intensive. New renderings have to be compared to reference renderings whenever a change is introduced into the render system. To speed up this test process, unit testing can be applied. But only detecting differences at the pixel level is not enough. For instance, in the context of games or scientific visualization, we are often faced with random procedurally generated geometry like e.g. particle systems, waving water, plants or molecules. Therefore, a more sophisticated approach than a pixelwise comparison is needed. We propose a Smart Image Quality Assessment Algorithm (SIQA) based on a self-organizing map which can handle random scene elements. We compare our method with traditional image quality assessment methods like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Maps (SSIM). The proposed method helps to prevent the detection of images being categorized wrongly as correct or having errors, and ultimately helps saving time and increases productivity in the context of a test-driven development process for rendering algorithms.
Error Metrics for Smart Image Refinement
Julian Amann¹, Matthäus G. Chajdas¹, Rüdiger Westermann¹, Journal of WSCG, Vol.20, No.1-3, pp. 127-135, ISSN 1213-6972, Union Agency, 2012
¹Technische Universität München
Scanline rasterization is still the dominating approach in real-time rendering. For performance reasons, realtime ray tracing is only used in special applications. However, ray tracing computes better shadows, reflections, refractions, depth-of-field and various other visual effects, which are hard to achieve with a scanline rasterizer. A hybrid rendering approach benefits from the high performance of a rasterizer and the quality of a ray tracer. In this work, a GPU-based hybrid rasterization and ray tracing system that supports reflections, depth-of-field and shadows is introduced. The system estimates the quality improvement that a ray tracer could achieve in comparison to a rasterization based approach. Afterwards, regions of the rasterized image with a high estimated quality improvement index are refined by ray tracing.