Real-time Kd-tree Based Importance Sampling of Environment Maps

Serkan Ergun, Murat Kurt, Aydin Ozturk
International Computer Institute, Ege University
Spring conference on computer graphics, 2012
@inproceedings{EKO12,

   title={Real-time Kd-tree Based Importance Sampling of Environment Maps},

   author={Ergun, Serkan and Kurt, Murat and {"O}zt{"u}rk, Ayd{i}n},

   booktitle={Proceedings of the 28th Spring Conference on Computer Graphics},

   pages={84–91},

   year={2012},

   organization={Comenius University Press}

}

Download Download (PDF)   View View   Source Source   
We present a new real-time importance sampling algorithm for environment maps. Our method is based on representing environment maps using kd-tree structures, and generating samples with a single data lookup. An efficient algorithm has been developed for realtime image-based lighting applications. In this paper, we compared our algorithm with Inversion method [Fishman 1996]. We show that our proposed algorithm provides compactness and speedup as compared to Inversion method. Based on a number of rendered images, we have demonstrated that in a fixed time frame the proposed algorithm produces images with a lower noise than that of the Inversion method. We also demonstrate that our algorithm can successfully represent a wide range of material types.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

You must be logged in to post a comment.

* * *

* * *

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us:

contact@hgpu.org