Demo Data from EDBT 2026 / reviewers in the wild / expert
Rahul Khardekar
dblp:57/5306
· DBLP profile ↗
7ranked-venue papers
3as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 7 · 3 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
3 papers |
Geometric modeling and processing · 70% Computational fabrication · 23% Rendering · 7% | |
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
GPUs and heterogeneous computing · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › computer-aided design › computer-aided geometric design › NURBS modeling
NURBS surface modeling |
0.1 | 1 | 2009 | Performing Efficient NURBS Modeling Operations on the GPU · IEEE Trans. Vis. Comput. Graph. 2009 |
Geometric modeling and processing › surface processing
surface intersection |
0.1 | 1 | 2009 | Performing Efficient NURBS Modeling Operations on the GPU · IEEE Trans. Vis. Comput. Graph. 2009 |
Computational fabrication › mechanical design
mold design |
0.1 | 1 | 2006 | Finding feasible mold parting directions using graphics hardware · Comput. Aided Des. 2006 |
GPUs and heterogeneous computing › GPU computing › GPGPU acceleration
GPU acceleration of geometric computation |
0.0 | 1 | 2009 | Performing Efficient NURBS Modeling Operations on the GPU · IEEE Trans. Vis. Comput. Graph. 2009 |
GPUs and heterogeneous computing
GPU computing |
0.0 | 1 | 2009 | Optimized GPU evaluation of arbitrary degree NURBS curves and surfaces · Comput. Aided Des. 2009 |
Rendering
GPU rendering |
0.0 | 1 | 2006 | Finding feasible mold parting directions using graphics hardware · Comput. Aided Des. 2006 |
Methods — techniques the papers use, named apart from their topics
programmable fragment processor · 0.2NURBS evaluation · 0.2graphics hardware · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Efficient computation of a near-optimal primary parting lineabstractIn injection molding, a flat parting surface that is normal to the mold parting direction achieves the best mold alignment with the least cost. However, for complex parts, parting surfaces that consist of a number of planes are necessary. In this paper, we provide an algorithm to find a near-optimal parting surface as a series of planes that intersect the boundary of the part.We form a continuous band of triangles on the part boundary that are parallel to the parting direction within a tolerance and perform vertical trapezoidation of the band. We can then find a set of planes that intersect vertical lines in the trapezoidation. We use a linear program to keep the planes normal to the parting direction. Rahul Khardekar, Sara McMains |
Symposium on Solid and Physical Modeling | 1 |
| 2009 | Optimized GPU evaluation of arbitrary degree NURBS curves and surfaces
Adarsh Krishnamurthy, Rahul Khardekar, Sara McMains |
Comput. Aided Des. | 2 |
| 2009 | Performing Efficient NURBS Modeling Operations on the GPUabstractWe present algorithms for evaluating and performing modeling operations on NURBS surfaces using the programmable fragment processor on the Graphics Processing Unit (GPU). We extend our GPU-based NURBS evaluator that evaluates NURBS surfaces to compute exact normals for either standard or rational B-spline surfaces for use in rendering and geometric modeling. We build on these calculations in our new GPU algorithms to perform standard modeling operations such as inverse evaluations, ray intersections, and surface-surface intersections on the GPU. Our modeling algorithms run in real time, enabling the user to sketch on the actual surface to create new features. In addition, the designer can edit the surface by interactively trimming it without the need for retessellation. Our GPU-accelerated algorithm to perform surface-surface intersection operations with NURBS surfaces can output intersection curves in the model space as well as in the parametric spaces of both the intersecting surfaces at interactive rates. We also extend our surface-surface intersection algorithm to evaluate self-intersections in NURBS surfaces. Adarsh Krishnamurthy, Rahul Khardekar, Sara McMains, Kirk Haller, Gershon Elber |
IEEE Trans. Vis. Comput. Graph. | 2 |
| 2008 | Performing efficient NURBS modeling operations on the GPUabstractWe present algorithms for evaluating and performing modeling operatyons on NURBS surfaces using the programmable fragment processor on the Graphics Processing Unit (GPU). We extend our GPU-based NURBS evaluator that evaluates NURBS surfaces to compute exact normals for either standard or rational B-spline surfaces for use in rendering and geometric modeling. We build on these calculations in our new GPU algorithms to perform standard modeling operations such as inverse evaluations, ray intersections, and surface-surface intersections on the GPU. Our modeling algorithms run in real time, enabling the user to sketch on the actual surface to create new features. In addition, the designer can edit the surface by interactively trimming it without the need for re-tessellation. We also present a GPU-accelerated algorithm to perform surface-surface intersection operations with NURBS surfaces that can output intersection curves in the model space as well as in the parametric spaces of both the intersecting surfaces at interactive rates. Adarsh Krishnamurthy, Rahul Khardekar, Sara McMains, Kirk Haller, Gershon Elber |
Symposium on Solid and Physical Modeling | 2 |
| 2007 | Direct evaluation of NURBS curves and surfaces on the GPUabstractThis paper presents a new method to evaluate and display trimmed NURBS surfaces using the Graphics Processing Unit (GPU). Trimmed NURBS surfaces, the de facto standard in commercial 3D CAD modeling packages, are currently tessellated into triangles before being sent to the graphics card for display since there is no native hardware support for NURBS. Previous GPU-based NURBS display methods relied on first approximating the NURBS patches with lower degree Bezier patches before evaluation. Our method uses a GPU fragment program to evaluate the surface point coordinates of the original NURBS patch directly, from the control points and knot vectors stored as textures in graphics memory. This evaluated surface is trimmed during display using a dynamically generated trim-texture calculated via alpha blending. The implementation incorporates dynamic Level of Detail (LOD) for real-time interaction at different resolutions of the NURBS surfaces. We obtain rendering speeds at least one order of magnitude faster than evaluation using the CPU. Adarsh Krishnamurthy, Rahul Khardekar, Sara McMains |
Symposium on Solid and Physical Modeling | 2 |
| 2006 | Finding feasible mold parting directions using graphics hardware
Rahul Khardekar, Greg Burton, Sara McMains |
Comput. Aided Des. | 1 |
| 2005 | Finding feasible mold parting directions using graphics hardwareabstractWe present new programmable graphics hardware accelerated algorithms to test the castability of geometric parts and assist with part redesign. These algorithms efficiently identify and graphically display undercuts and minimum and insufficient draft angles. Their running times grow only linearly with respect to the number of facets in the solid model, making them efficient subroutines for our algorithms that test whether a tessellated CAD model can be manufactured in a two-part mold. We have developed and implemented two such algorithms to choose candidate directions to test for castability using accessibility analysis and Gauss maps. The efficiency of these algorithms lies in that they identify groups of candidate directions such that if any one direction in the group is not castable, none are, or if any one is castable, all are. We examine trade-offs between the algorithms' speed, accuracy, and whether they guarantee that a castable direction will be found for a part if one exists. Rahul Khardekar, Greg Burton, Sara McMains |
Symposium on Solid and Physical Modeling | 1 |