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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Fast Global Illumination on Dynamic Height FieldsDerek Nowrouzezahrai and John Snyder AbstractWe present a real-time method for rendering global illumination effects from large area and environmental lights on dynamic height fields. In contrast to previous work, our method handles inter-reflections (indirect lighting) and non-diffuse surfaces. To reduce sampling, we construct one multi-resolution pyramid for height variation to compute direct shadows, and another pyramid for each indirect bounce of incident radiance to compute interreflections. The basic principle is to sample the points blocking direct light, or shedding indirect light, from coarser levels of the pyramid the farther away they are from a given receiver point. We unify the representation of visibility and indirect radiance at discrete azimuthal directions (i.e., as a function of a single elevation angle) using the concept of a casting set of visible points along this direction whose contributions are collected in the basis of normalized Legendre polynomials. This analytic representation is compact, requires no precomputation, and allows efficient integration to produce the spherical visibility and indirect radiance signals. Sub-sampling visibility and indirect radiance, while shading with full-resolution surface normals, further increases performance without introducing noticeable artifacts. Our method renders 512x512 height fields (>500K triangles) at 36Hz. CitationDerek Nowrouzezahrai and John Snyder. Fast global illumination on dynamic height fields. Computer Graphics Forum: Eurographics Symposium on Rendering, June 2009. [BiBTeX] Links
Data-driven curvature for real-time line drawing of dynamic scenesEvangelos Kalogerakis, Derek Nowrouzezahrai, Patricio Simari, James Mccrae, Aaron Hertzmann, and Karan Singh AbstractThis paper presents a method for real-time line drawing of deforming objects. Object-space line drawing algorithms for many types of curves, including suggestive contours, highlights, ridges and valleys, rely on surface curvature and curvature derivatives. Unfortunately, these curvatures and their derivatives cannot be computed in real-time for animated, deforming objects. In a preprocessing step, our method learns the mapping from a low-dimensional set of animation parameters (e.g., joint angles) to surface curvatures for a deforming 3D mesh. The learned model can then accurately and efficiently predict curvatures and their derivatives, enabling real-time object-space rendering of suggestive contours and other such curves. This represents an order-of-magnitude speed-up over the fastest existing algorithm capable of estimating curvatures and their derivatives accurately enough for many different types of line drawings. The learned model can generalize to novel animation sequences, and is also very compact, typically requiring a few megabytes of storage at run-time. We demonstrate our method for various types of animated objects, including skeleton-based characters, cloth simulation and blend-shape facial animation, using a variety of non-photorealistic rendering styles.An important component of our system is the use of dimensionality reduction for differential mesh data. CitationEvangelos Kalogerakis, Derek Nowrouzezahrai, Patricio Simari, James Mccrae, Aaron Hertzmann, and Karan Singh. Data-driven curvature for real-time line drawing of dynamic scenes. ACM Transactions on Graphics, 28(1):1–13, May 2009. [BiBTeX] Links
Shadowing Dynamic Scenes with Arbitrary BRDFsDerek Nowrouzezahrai, Evangelos Kalogerakis, and Eugene Fiume AbstractWe present a real-time relighting and shadowing method for dynamic scenes with varying lighting, view and BRDFs. Our approach is based on a compact representation of reflectance data that allows for changing the BRDF at run-time and a data-driven method for accurately synthesizing self-shadows on articulated and deformable geometries. Unlike previous self-shadowing approaches, we do not rely on local blocking heuristics. We do not fit a model to the BRDF-weighted visibility, but rather only to the visibility that changes during animation. In this manner, our model is more compact than previous techniques and requires less computation both during fitting and at run-time. Our reflectance product operators can re-integrate arbitrary low-frequency view-dependent BRDF effects on-the-fly and are compatible with all previous dynamic visibility generation techniques as well as our own data-driven visibility model. We apply our reflectance product operators to three different visibility generation models, and our data-driven model can achieve framerates well over 300Hz. CitationDerek Nowrouzezahrai, Evangelos Kalogerakis, and Eugene Fiume. Shadowing dynamic scenes with arbitrary brdfs. Computer Graphics Forum: Eurographics Conference, 28:249–258(10), April 2009. [BiBTeX] Links
Multi-objective shape segmentation and labelingPatricio Simari, Derek Nowrouzezahrai, Evangelos Kalogerakis, and Karan Singh AbstractShape segmentations designed for different applications show significant variation in the composition of their parts. In this paper, we introduce the segmentation and labeling of shape based on the optimization of multiple objectives that capture application-specific segmentation criteria. We present a number of efficient objective functions that capture useful shape adjectives (compact, flat, narrow, perpendicular, etc.) Segmentation descriptions within our framework combine multiple such objective functions with optional labels to define each part. The optimization problem is simplified by proposing weighted Voronoi partitioning as a compact and continuous parameterization of spatially embedded shape segmentations. Separation of spatially close but geodesically distant parts is made possible using multi-dimensional scaling (MDS) prior to Voronoi partitioning. Optimization begins with an initial segmentation found using the centroids of a k-means clustering of surface elements. This partition is automatically labeled to optimize heterogeneous part objectives and the Voronoi centers and their weights optimized using Generalized Pattern Search (GPS). We illustrate our framework using several diverse segmentation applications: consistent segmentations with semantic labels, bounding volume hierarchies for path tracing, and automatic rig and clothing transfer between animation characters. CitationPatricio Simari, Derek Nowrouzezahrai, Evangelos Kalogerakis, and Karan Singh. Multi-objective shape segmentation and labeling. In Computer Graphics Forum: Eurographics Symposium on Geometry Processing, Aire-la-Ville, Switzerland, Switzerland, March 2009. Eurographics Association. [BiBTeX] Links
Extracting lines of curvature from noisy point cloudsEvangelos Kalogerakis, Derek Nowrouzezahrai, Patricio Simari, and Karan Singh AbstractWe present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds. CitationEvangelos Kalogerakis, Derek Nowrouzezahrai, Patricio Simari, and Karan Singh. Extracting lines of curvature from noisy point clouds. Computer Aided Design, 41(4):282–292, January 2009. [BiBTeX] Links
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