CFP last date
20 December 2024
Reseach Article

The Science of Ray Tracing

by Dhruv Dhote, Charu Virmani, K. Gopi Krishna, Shivansh Raghav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 42
Year of Publication: 2020
Authors: Dhruv Dhote, Charu Virmani, K. Gopi Krishna, Shivansh Raghav
10.5120/ijca2020920443

Dhruv Dhote, Charu Virmani, K. Gopi Krishna, Shivansh Raghav . The Science of Ray Tracing. International Journal of Computer Applications. 176, 42 ( Jul 2020), 15-20. DOI=10.5120/ijca2020920443

@article{ 10.5120/ijca2020920443,
author = { Dhruv Dhote, Charu Virmani, K. Gopi Krishna, Shivansh Raghav },
title = { The Science of Ray Tracing },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 42 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number42/31482-2020920443/ },
doi = { 10.5120/ijca2020920443 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:07.269697+05:30
%A Dhruv Dhote
%A Charu Virmani
%A K. Gopi Krishna
%A Shivansh Raghav
%T The Science of Ray Tracing
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 42
%P 15-20
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The holy grail of rendering light and shadow in a scene by simulating and tracking every ray of light to blend in CG work with real-life scenes. It is the upcoming technology behind computer graphics for films and games to produce incredible realistic scenes in the computer generated world. The developers should utilize simulation for the precise prediction of the illuminance of the graphics to be used in films or games. This study digs the techniques and algorithms known for ray tracing and analyses the results Radiance and Lightscape 3.2 for a practical designed technique.

References
  1. Aubry, M., Maturana, D., Efros, A.A., Russell, B.C., Sivic, J.: Seeing 3D chairs: Exemplar part-based 2D-3D alignment using a large dataset of CAD models. In: CVPR (2014).
  2. Aubry, M., Russell, B.C.: Understanding deep features with computer-generated imagery. In: ICCV
  3. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. IJCV 92(1) (2011).
  4. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The Cityscapes dataset for semantic urban scene understanding. In:CVPR (2016).
  5. Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., vander Smagt, ¨., Cremers, D., Brox, T.: FlowNet: Learning optical flow with convolutional networks. In: ICCV (2015).
  6. Chen, C., Seff, A., Kornhauser, A.L., Xiao, J.: DeepDriving: Learning affordance for direct perception in autonomous driving. In: ICCV (2015).
  7. Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large ¨ dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR (2016).
  8. Richter, S.R., Roth, S.: Discriminative shape from shading in uncalibrated illumination. In: CVPR (2015).
  9. Tripathi, S., Belongie, S., Hwang, Y., Nguyen, T.Q.: Semantic video segmentation: Exploring inference efficiency. In: ISOCC (2015).
  10. Xie, J., Kiefel, M., Sun, M.T., Geiger, A.: Semantic instance annotation of street scenes by 3D to 2D label transfer. In: CVPR (2016)
  11. Xu, J., Vazquez, D., L ´ opez, A.M., Mar ´ ´ın, J., Ponsa, D.: Learning a part-based pedestrian detector in a virtual world. IEEE Transactions on Intelligent Transportation Systems 15(5) (2014)
  12. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
  13. Lum, E. B., Montrym, J. S., Steiner, W. R., Cobb, J., & Moreton, H. P. (2018). U.S. Patent No. 9,947,084. Washington, DC: U.S. Patent and Trademark Office.
  14. Wynters, E. (2011). Parallel processing on NVIDIA graphics processing units using CUDA. Journal of Computing Sciences in Colleges, 26(3), 58-66.
  15. Komatitsch, D., Michéa, D., & Erlebacher, G. (2009). Porting a high-order finite-element earthquake modeling application to NVIDIA graphics cards using CUDA. Journal of Parallel and Distributed Computing, 69(5), 451-460.
  16. Wald, I., Usher, W., Morrical, N., Lediaev, L., & Pascucci, V. (2019). RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location. Proceedings of High Performance Graphics.
  17. Sanzharov, V. V., Gorbonosov, A. I., Frolov, V. A., & Voloboy, A. G. (2019). Examination of the Nvidia RTX.
  18. Deng Y. et al. Toward real-time ray tracing: A survey on hardware acceleration and microarchitecture tech-niques //ACM Computing Surveys (CSUR). – 2017. – . 50. – №. 4. p. 58.
  19. Gribble C. P., Ramani K. Coherent ray tracing via stream filtering //2008 IEEE Symposium on Interac-tive Ray Tracing. – IEEE, 2008. – p. 59-66.
  20. Hall. D. The AR350: Today’s ray trace rendering processor. //Eurographics/SIGGRAPH workshop on Graphics hardware - Hot 3D Session 1, 2001.
  21. Kajiya J. T. The rendering equation //ACM SIG-GRAPH computer graphics. – ACM, 1986. – . 20. –№. 4. – p. 143-150.
  22. Keely S. Reduced precision hardware for ray tracing.//Proc.HPG. – 2014. – p. 29-40.
  23. Kopta D. et al. An energy and bandwidth efficient ray tracing architecture //High-performance Graphics. –ACM, 2013. – p. 121-128.
  24. Lee W. J. et al. SGRT: A mobile GPU architecture for real-time ray tracing //High-performance graphics conference. – ACM, 2013. – p. 109-119.
Index Terms

Computer Science
Information Sciences

Keywords

Raytracing