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Sashidhar Guntury

dblp:41/9380 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 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
1 paper
Rendering · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › ray tracing
acceleration structure
0.112012
Raytracing Dynamic Scenes on the GPU Using Grids · IEEE Trans. Vis. Comput. Graph. 2012
Rendering
ray tracing
0.112012
Raytracing Dynamic Scenes on the GPU Using Grids · IEEE Trans. Vis. Comput. Graph. 2012
GPUs and heterogeneous computing
GPU computing
0.012012
Raytracing Dynamic Scenes on the GPU Using Grids · IEEE Trans. Vis. Comput. Graph. 2012
GPUs and heterogeneous computing › GPU rendering
GPU ray tracing
0.012012
Raytracing Dynamic Scenes on the GPU Using Grids · IEEE Trans. Vis. Comput. Graph. 2012

Methods — techniques the papers use, named apart from their topics

spherical grids · 0.3independent voxel walk · 0.3grid acceleration structures · 0.3enforced coherence · 0.3perspective grids · 0.1perspective grid · 0.1
YearPublicationVenuePosition
2012 Raytracing Dynamic Scenes on the GPU Using Grids
abstract
Raytracing dynamic scenes at interactive rates have received a lot of attention recently. We present a few strategies for high performance raytracing on a commodity GPU. The construction of grids needs sorting, which is fast on today's GPUs. The grid is thus the acceleration structure of choice for dynamic scenes as per-frame rebuilding is required. We advocate the use of appropriate data structures for each stage of raytracing, resulting in multiple structure building per frame. A perspective grid built for the camera achieves perfect coherence for primary rays. A perspective grid built with respect to each light source provides the best performance for shadow rays. Spherical grids handle lights positioned inside the model space and handle spotlights. Uniform grids are best for reflection and refraction rays with little coherence. We propose an Enforced Coherence method to bring coherence to them by rearranging the ray to voxel mapping using sorting. This gives the best performance on GPUs with only user-managed caches. We also propose a simple, Independent Voxel Walk method, which performs best by taking advantage of the L1 and L2 caches on recent GPUs. We achieve over 10 fps of total rendering on the Conference model with one light source and one reflection bounce, while rebuilding the data structure for each stage. Ideas presented here are likely to give high performance on the future GPUs as well as other manycore architectures.
Sashidhar Guntury, P. J. Narayanan
IEEE Trans. Vis. Comput. Graph.1