e-mail: my first name@dgp.toronto.edu
work phone: +1 (416) 946-8427
| Home | Publications (brief) | All Abstracts & Files | Academic Service | Completed Internships |
| I am a Ph.D candidate in the Dynamic Graphics Project lab at the University of Toronto. I completed a BASc in Honours Computer Engineering from the University of Waterloo in May 2005 and an MSc in Computer Science from the University of Toronto in October 2006. My research interests are realistic image synthesis, high-performance rendering using commodity graphics hardware and methods for real-time fluid simulation and animation. My supervisor is Dr. Eugene Fiume. I completed an internship at MSR Redmond in 2008 working on interactive illumination effects under the supervision of Dr. John Snyder. I will be conducting research at Disney Research Zurich as an intern during the summer of 2009 with Dr. Wojciech Jarosz. |
e-mail: my first name@dgp.toronto.edu work phone: +1 (416) 946-8427 |
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Compact and efficient generation of radiance transfer for dynamically articulated charactersDerek Nowrouzezahrai, Patricio Simari, Evangelos Kalogerakis, Karan Singh, and Eugene Fiume AbstractWe present a data-driven technique for generating the precomputed radiance transfer vectors of an animated character as a function of its joint angles. We learn a linear model for generating real-time lighting effects on articulated characters while capturing soft self shadows caused by dynamic distant lighting. Indirect illumination can also be reproduced using our framework. Previous data-driven techniques have either restricted the type of lighting response (generating only ambient occlusion), the type of animated sequences (response functions to external forces) or have complicated runtime algorithms and incur non-trivial memory costs. We provide insights into the dimensionality reduction of the pose and coefficient spaces. Our model can be fit quickly as a preprocess, is very compact (~1MB) and runtime transfer vectors are generated using a simple algorithm in real-time (>100 Hz using a CPU-only implementation.) We can reproduce lighting effects on hundreds of trained poses using less memory than required to store a single mesh's PRT coefficients. Moreover, our model extrapolates to produce smooth, believable lighting results on novel poses and our method can be easily integrated into existing interactive content pipelines. CitationDerek Nowrouzezahrai, Patricio Simari, Evangelos Kalogerakis, Karan Singh, and Eugene Fiume. Compact and efficient generation of radiance transfer for dynamically articulated characters. In GRAPHITE '07: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, pages 147–154, New York, NY, USA, January 2007. ACM. [BiBTeX] Links
Eigentransport for efficient and accurate all-frequency relightingDerek Nowrouzezahrai, Patricio Simari, Evangelos Kalogerakis, and Eugene Fiume Winner: Best paper award!AbstractWe present a method for creating a geometry-dependent basis for diffuse precomputed radiance transfer. Unlike previous PRT bases, ours is derived from Principal Component Analysis of the sampled transport functions at each vertex, without relying on pre-projections to secondary bases, such as the Spherical Harmonics or Haar wavelets. It allows for efficient evaluation of shading, has low memory requirements and produces accurate results with few coefficients. We are able to capture all-frequency effects from both distant and near-field dynamic lighting in real-time and present a simple and efficient rotation scheme. Reconstruction of the final shading becomes a low-order dot product and is performed on the GPU. CitationDerek Nowrouzezahrai, Patricio Simari, Evangelos Kalogerakis, and Eugene Fiume. Eigentransport for efficient and accurate all-frequency relighting. In GRAPHITE '07: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, pages 163–169, New York, NY, USA, January 2007. ACM. [BiBTeX] Links
Image-Based Proxy Accumulation for Real-Time Soft Global IlluminationPeter-Pike Sloan, Naga K. Govindaraju, Derek Nowrouzezahrai, and John Snyder AbstractWe present a new, general, and real-time technique for soft global illumination in low-frequency environmental lighting. It accumulates over relatively few spherical proxies which approximate the light blocking and re-radiating effect of dynamic geometry. Soft shadows are computed by accumulating log visibility vectors for each sphere proxy as seen by each receiver point. Inter-reflections are computed by accumulating vectors representing the proxy's unshadowed radiance when illuminated by the environment. Both vectors capture low-frequency directional dependence using the spherical harmonic basis. We also present a new proxy accumulation strategy that splats each proxy to receiver pixels in image space to collect its shadowing and indirect lighting contribution. Our soft GI rendering pipeline unifies direct and indirect soft effects with a simple accumulation strategy that maps entirely to the GPU and outperforms previous vertex-based methods. CitationPeter-Pike Sloan, Naga K. Govindaraju, Derek Nowrouzezahrai, and John Snyder. Image-based proxy accumulation for real-time soft global illumination. Pacific Conference on Computer Graphics and Applications, 0:97–105, January 2007. [BiBTeX] Links
Robust statistical estimation of curvature on discretized surfacesEvangelos Kalogerakis, Patricio Simari, Derek Nowrouzezahrai, and Karan Singh AbstractA robust statistics approach to curvature estimation on discretely sampled surfaces, namely polygon meshes and point clouds, is presented. The method exhibits accuracy, stability and consistency even for noisy, non-uniformly sampled surfaces with irregular configurations. Within an M-estimation framework, the algorithm is able to reject noise and structured outliers by sampling normal variations in an adaptively reweighted neighborhood around each point. The algorithm can be used to reliably derive higher order differential attributes and even correct noisy surface normals while preserving the fine features of the normal and curvature field. The approach is compared with state-of-the-art curvature estimation methods and shown to improve accuracy by up to an order of magnitude across ground truth test surfaces under varying tessellation densities and types as well as increasing degrees of noise. Finally, the benefits of a robust statistical estimation of curvature are illustrated by applying it to the popular applications of mesh segmentation and suggestive contour rendering. CitationEvangelos Kalogerakis, Patricio Simari, Derek Nowrouzezahrai, and Karan Singh. Robust statistical estimation of curvature on discretized surfaces. In Computer Graphics Forum: Eurographics Symposium on Geometry Processing, pages 13–22, Aire-la-Ville, Switzerland, Switzerland, January 2007. Eurographics Association. [BiBTeX] Links
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