Abstract
Foveated rendering synthesizes images with progressively less detail outside the eye fixation region, potentially unlocking significant speedups for wide field-of-view displays, such as head mounted displays, where target framerate and resolution is increasing faster than the performance of traditional real-time renderers.
To study and improve potential gains, we designed a foveated rendering user study to evaluate the perceptual abilities of human peripheral vision when viewing today's displays. We determined that filtering peripheral regions reduces contrast, inducing a sense of tunnel vision. When applying a postprocess contrast enhancement, subjects tolerated up to 2× larger blur radius before detecting differences from a non-foveated ground truth. After verifying these insights on both desktop and head mounted displays augmented with high-speed gaze-tracking, we designed a perceptual target image to strive for when engineering a production foveated renderer.
Given our perceptual target, we designed a practical foveated rendering system that reduces number of shades by up to 70% and allows coarsened shading up to 30° closer to the fovea than Guenter et al. [2012] without introducing perceivable aliasing or blur. We filter both pre- and post-shading to address aliasing from undersampling in the periphery, introduce a novel multiresolution- and saccade-aware temporal antialising algorithm, and use contrast enhancement to help recover peripheral details that are resolvable by our eye but degraded by filtering.
We validate our system by performing another user study. Frequency analysis shows our system closely matches our perceptual target. Measurements of temporal stability show we obtain quality similar to temporally filtered non-foveated renderings.
Supplemental Material
Available for Download
Supplemental file.
- Baker, D., 2016. Object space lighting - following film rendering 2 decades later in real time, 03. Game Developers Conference Talk.Google Scholar
- Banks, M. S., Sekuler, A. B., and Anderson, S. J. 1991. Peripheral spatial vision: limits imposed by optics, photoreceptors, and receptor pooling. Journal of the Optical Society of America A 8, 11, 1775--1787.Google ScholarCross Ref
- Banks, M. S., Gepshtein, S., and Landy, M. S. 2004. Why is spatial stereoresolution so low? The Journal of Neuroscience 24, 9, 2077--2089.Google ScholarCross Ref
- Clarberg, P., Toth, R., Hasselgren, J., Nilsson, J., and Akenine-Möller, T. 2014. Amfs: adaptive multi-frequency shading for future graphics processors. ACM Transactions on Graphics 33, 4, 141:1--141:12. Google ScholarDigital Library
- Cowey, A., and Rolls, E. T. 1974. Human cortical magnification factor and its relation to visual acuity. Experimental Brain Research 21, 5, 447--454.Google ScholarCross Ref
- Curcio, C. A., and Allen, K. A. 1990. Topography of ganglion cells in human retina. Journal of Comparative Neurology 300, 1, 5--25.Google ScholarCross Ref
- Curcio, C. A., Sloan, K. R., Kalina, R. E., and Hendrickson, A. E. 1990. Human photoreceptor topography. Journal of Comparative Neurology 292, 4, 497--523.Google ScholarCross Ref
- Ferree, C. E., Rand, G. G., and Hardy, C. C. 1931. Refraction for the peripheral field of vision. Archives of Ophthalmology 5, 5, 717--731.Google ScholarCross Ref
- Green, C. 2007. Improved alpha-tested magnification for vector textures and special effects. In ACM SIGGRAPH Courses, SIGGRAPH, 9--18. Google ScholarDigital Library
- Grundland, M., Vohra, R., Williams, G. P., and Dodgson, N. A. 2006. Cross Dissolve Without Cross Fade: Preserving Contrast, Color and Salience in Image Compositing. Computer Graphics Forum 25, 3, 577--586.Google ScholarCross Ref
- Guenter, B., Finch, M., Drucker, S., Tan, D., and Snyder, J. 2012. Foveated 3D graphics. ACM Transactions on Graphics 31, 6, 164:1--164:10. Google ScholarDigital Library
- Hansen, T., Pracejus, L., and Gegenfurtner, K. R. 2009. Color perception in the intermediate periphery of the visual field. Journal of Vision 9, 4, 26:1--26:12.Google ScholarCross Ref
- He, Y., Gu, Y., and Fatahalian, K. 2014. Extending the graphics pipeline with adaptive, multi-rate shading. ACM Transactions on Graphics 33, 4, 142:1--142:12. Google ScholarDigital Library
- Hill, S., McAuley, S., Burley, B., Chan, D., Fascione, L., Iwanicki, M., Hoffman, N., Jakob, W., Neubelt, D., Pesce, A., and Pettineo, M. 2015. Physically based shading in theory and practice. In ACM SIGGRAPH Courses, SIGGRAPH, 22:1--22:8. Google ScholarDigital Library
- Hillesland, K. E., and Yang, J. C. 2016. Texel Shading. In EG 2016 - Short Papers, The Eurographics Association, T. Bashford-Rogers and L. P. Santos, Eds.Google Scholar
- Jimenez, J., Echevarria, J. I., Sousa, T., and Gutierrez, D. 2012. SMAA: Enhanced morphological antialiasing. Computer Graphics Forum (Proc. EUROGRAPHICS 2012) 31, 2. Google ScholarDigital Library
- Kaplanyan, A., Hill, S., Patney, A., and Lefohn, A. 2016. Filtering distributions of normals for shading antialiasing. In Proceedings of the Symposium on High-Performance Graphics. Google ScholarDigital Library
- Karis, B. 2014. High-quality temporal supersampling. In Advances in Real-Time Rendering in Games, SIGGRAPH Courses.Google Scholar
- Kelly, D. H., and Savoie, R. E. 1973. A study of sine-wave contrast sensitivity by two psychophysical methods. Perception & Psychophysics 14, 2, 313--318.Google ScholarCross Ref
- Kelly, D. H. 1984. Retinal inhomogeneity. i. spatiotemporal contrast sensitivity. Journal of the Optical Society of America A 1, 1, 107--113.Google ScholarCross Ref
- Kim, M. H., Ritschel, T., and Kautz, J. 2011. Edge-aware color appearance. ACM Transactions on Graphics 30, 2, 13:1--13:9. Google ScholarDigital Library
- Koenderink, J. J., Bouman, M. A., Bueno de Mesquita, A. E., and Slappendel, S. 1978. Perimetry of contrast detection thresholds of moving spatial sine patterns. II. The far peripheral visual field (eccentricity 0 degrees-50 degrees). Journal of the Optical Society of America A 68, 6, 850--854.Google ScholarCross Ref
- Koenderink, J. J., Bouman, M. A., Bueno de Mesquita, A. E., and Slappendel, S. 1978. Perimetry of contrast detection thresholds of moving spatial sine wave patterns. I. The near peripheral visual field (eccentricity 0 degrees-8 degrees). Journal of the Optical Society of America A 68, 6, 845--849.Google ScholarCross Ref
- Koenderink, J. J., Bouman, M. A., Bueno de Mesquita, A. E., and Slappendel, S. 1978. Perimetry of contrast detection thresholds of moving spatial sine wave patterns. III. The target extent as a sensitivity controlling parameter. Journal of the Optical Society of America A 68, 6, 854--860.Google ScholarCross Ref
- Lauritzen, A., Salvi, M., and Lefohn, A. 2011. Sample distribution shadow maps. In Symposium on Interactive 3D Graphics and Games, 97--102. Google ScholarDigital Library
- Levi, D. M., Klein, S. A., and Aitsebaomo, P. 1985. Vernier acuity, crowding and cortical magnification. Vision Research 25, 7, 963--977.Google ScholarCross Ref
- Levitt, H. 1971. Transformed up-down methods in psychoacoustics. The Journal of the Acoustical society of America 49, 2B, 467--477.Google ScholarCross Ref
- McKee, S. P., and Nakayama, K. 1984. The detection of motion in the peripheral visual field. Vision Research 24, 1, 25--32.Google ScholarCross Ref
- Mäkelä, P., Näsänen, R., Rovamo, J., and Melmoth, D. 2001. Identification of facial images in peripheral vision. Vision Research 41, 5, 599--610.Google ScholarCross Ref
- Navarro, R., Artal, P., and Williams, D. R. 1993. Modulation transfer of the human eye as a function of retinal eccentricity. Journal of the Optical Society of America A 10, 2, 201--212.Google ScholarCross Ref
- Noorlander, C., Koenderink, J. J., Olden, R. J. D., and Edens, B. W. 1983. Sensitivity to spatiotemporal colour contrast in the peripheral visual field. Vision Research 23, 1, 1--11.Google ScholarCross Ref
- Olano, M., and Baker, D. 2010. Lean mapping. In Symposium on Interactive 3D Graphics and Games, 181--188. Google ScholarDigital Library
- Öztireli, A. C., and Gross, M. 2015. Perceptually based downscaling of images. ACM Transactions on Graphics 34, 4, 77:1--77:10. Google ScholarDigital Library
- Patney, A., Kim, J., Salvi, M., Kaplanyan, A., Wyman, C., Benty, N., Lefohn, A., and Luebke, D. 2016. Perceptually-based foveated virtual reality. In ACM SIGGRAPH 2016 Emerging Technologies, ACM, New York, NY, USA, SIGGRAPH '16, 17:1--17:2. Google ScholarDigital Library
- Pharr, M., and Humphreys, G. 2010. Physically Based Rendering, Second Edition: From Theory to Implementation, 2nd ed. Morgan Kaufmann Publishers, Inc. Google ScholarDigital Library
- Rosén, R. 2013. Peripheral Vision: Adaptive Optics and Psychophysics. PhD thesis, Royal Institute of Technology, Stockholm, Sweden.Google Scholar
- Rovamo, J., and Virsu, V. 1979. An estimation and application of the human cortical magnification factor. Experimental Brain Research 37, 3, 495--510.Google ScholarCross Ref
- Rovamo, J., Virsu, V., Laurinen, P., and Hyvärinen, L. 1982. Resolution of gratings oriented along and across meridians in peripheral vision. Investigative Ophthalmology & Visual Science 23, 5, 666--670.Google Scholar
- Salvi, M., and Vaidyanathan, K. 2014. Multi-layer alpha blending. In Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 151--158. Google ScholarDigital Library
- Schütt, H. H., Harmeling, S., Macke, J. H., and Wichmann, F. A. 2016. Painfree and accurate bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research 122, 105 -- 123.Google ScholarCross Ref
- Solomon, S. G., Lee, B. B., White, A. J., Ruttiger, L., and Martin, P. R. 2005. Chromatic organization of ganglion cell receptive fields in the peripheral retina. Journal of Neuroscience 25, 18, 4527--4539.Google ScholarCross Ref
- Strasburger, H., Rentschler, I., and Harvey, L. O. 1994. Cortical magnification theory fails to predict visual recognition. European Journal of Neuroscience 6, 10, 1583--1588.Google ScholarCross Ref
- Strasburger, H., Rentschler, I., and Jüttner, M. 2011. Peripheral vision and pattern recognition: A review. Journal of Vision 11, 5, 13:1--13:82.Google ScholarCross Ref
- Swafford, N. T., Iglesias-Guitian, J. A., Koniaris, C., Moon, B., Cosker, D., and Mitchell, K. 2016. User, metric, and computational evaluation of foveated rendering methods. In Proceedings of the ACM Symposium on Applied Perception, ACM, New York, NY, USA, SAP '16, 7--14. Google ScholarDigital Library
- Thibos, L. N., Cheney, F. E., and Walsh, D. J. 1987. Retinal limits to the detection and resolution of gratings. Journal of the Optical Society of America A 4, 8, 1524--1529.Google ScholarCross Ref
- Thibos, L., Walsh, D., and Cheney, F. 1987. Vision beyond the resolution limit: Aliasing in the periphery. Vision Research 27, 12, 2193--2197.Google ScholarCross Ref
- Thibos, L. N., Still, D. L., and Bradley, A. 1996. Characterization of spatial aliasing and contrast sensitivity in peripheral vision. Vision Research 36, 2, 249--258.Google ScholarCross Ref
- Thibos, L. N. 1987. Calculation of the influence of lateral chromatic aberration on image quality across the visual field. Journal of the Optical Society of America A 4, 8, 1673--1680.Google ScholarCross Ref
- Toth, R., Nilsson, J., and Akenine-Moller, T. 2016. Comparison of projection methods for rendering virtual reality. In Proceedings of the Symposium on High-Performance Graphics. Google ScholarDigital Library
- Vaidyanathan, K., Salvi, M., Toth, R., Foley, T., Akenine-Moller, T., Nilsson, J., Munkberg, J., Hasselgren, J., Sugihara, M., Clarberg, P., Janczak, T., and Lefohn, A. 2014. Coarse pixel shading. In Proceedings of the Symposium on High-Performance Graphics. Google ScholarDigital Library
- Wandell, B. A. 1995. Foundations of Vision. Sinauer Associates, Inc.Google Scholar
- Wang, Y.-Z., Thibos, L. N., and Bradley, A. 1996. Undersampling produces non-veridical motion perception, but not necessarily motion reversal, in peripheral vision. Vision Research 36, 12, 1737--1744.Google ScholarCross Ref
- Wang, Y.-Z., Bradley, A., and Thibos, L. N. 1997. Aliased frequencies enable the discrimination of compound gratings in peripheral vision. Vision Research 37, 3, 283--290.Google ScholarCross Ref
- Wichmann, F. A., and Hill, N. J. 2001. The psychometric function: I. fitting, sampling, and goodness of fit. Perception & Psychophysics 63, 8, 1293--1313.Google ScholarCross Ref
- Wichmann, F. A., and Hill, N. J. 2001. The psychometric function: Ii. bootstrap-based confidence intervals and sampling. Perception & Psychophysics 63, 8, 1314--1329.Google ScholarCross Ref
- Williams, D. R., Artal, P., Navarro, R., McMahon, M. J., and Brainard, D. H. 1996. Off-axis optical quality and retinal sampling in the human eye. Vision Research 36, 8, 1103--1114.Google ScholarCross Ref
- Williams, L. 1983. Pyramidal parametrics. SIGGRAPH Comput. Graph. 17, 3, 1--11. Google ScholarDigital Library
- Yang, L., Nehab, D., Sander, P. V., Sitthi-amorn, P., Lawrence, J., and Hoppe, H. 2009. Amortized supersampling. In ACM SIGGRAPH Asia 2009 Papers, ACM, New York, NY, USA, SIGGRAPH Asia '09, 135:1--135:12. Google ScholarDigital Library
Index Terms
- Towards foveated rendering for gaze-tracked virtual reality
Recommendations
Is Foveated Rendering Perceivable in Virtual Reality?: Exploring the Efficiency and Consistency of Quality Assessment Methods
MM '17: Proceedings of the 25th ACM international conference on MultimediaFoveated rendering leverages human visual system to increase video quality under limited computing resources for Virtual Reality (VR). More specifically, it increases the frame rate and the video quality of the foveal vision via lowering the resolution ...
Luminance-contrast-aware foveated rendering
Current rendering techniques struggle to fulfill quality and power efficiency requirements imposed by new display devices such as virtual reality headsets. A promising solution to overcome these problems is foveated rendering, which exploits gaze ...
User, metric, and computational evaluation of foveated rendering methods
SAP '16: Proceedings of the ACM Symposium on Applied PerceptionPerceptually lossless foveated rendering methods exploit human perception by selectively rendering at different quality levels based on eye gaze (at a lower computational cost) while still maintaining the user's perception of a full quality render. We ...
Comments