Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses
Journal Article
Casas, L., Fauconneau, M., Kosek, M., Mclister, K., & Mitchell, K. (2019)
Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses. Computers, 8(2), https://doi.org/10.3390/computers8020029
Shadow-retargeting maps depict the appearance of real shadows to virtual shadows given corresponding deformation of scene geometry, such that appearance is seamlessly maintain...
Error analysis of estimators that use combinations of stochastic sampling strategies for direct illumination
Journal Article
Subr, K., Nowrouzezahrai, D., Jarosz, W., Kautz, J., & Mitchell, K. (2014)
Error analysis of estimators that use combinations of stochastic sampling strategies for direct illumination. Computer Graphics Forum, 33(4), 93-102. https://doi.org/10.1111/cgf.12416
We present a theoretical analysis of error of combinations of Monte Carlo estimators used in image synthesis. Importance sampling and multiple importance sampling are popular ...
Guided ecological simulation for artistic editing of plant distributions in natural scenes
Journal Article
Bradbury, G. A., Subr, K., Koniaris, C., Mitchell, K., & Weyrich, T. (2015)
Guided ecological simulation for artistic editing of plant distributions in natural scenes. The Journal of Computer Graphics Techniques, 4(4),
In this paper we present a novel approach to author vegetation cover of large natural scenes. Unlike stochastic scatter-instancing tools for plant placement (such as multi-cla...
Adaptive rendering with linear predictions
Journal Article
Moon, B., Iglesias-Guitian, J. A., Yoon, S., & Mitchell, K. (2015)
Adaptive rendering with linear predictions. ACM transactions on graphics, 34(4), 121:1-121:11. https://doi.org/10.1145/2766992
We propose a new adaptive rendering algorithm that enhances the
performance of Monte Carlo ray tracing by reducing the noise, i.e.,
variance, while preserving a variety of hig...
Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering
Journal Article
Moon, B., Iglesias-Guitian, J. A., McDonagh, S., & Mitchell, K. (2017)
Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering. Computer Graphics Forum, 36(8), 600-612. https://doi.org/10.1111/cgf.13155
We propose a novel pre-filtering method that reduces the noise introduced by depth-of-field and motion blur effects in geometric
buffers (G-buffers) such as texture, normal an...
Adaptive polynomial rendering
Journal Article
Moon, B., McDonagh, S., Mitchell, K., & Gross, M. (2016)
Adaptive polynomial rendering. ACM transactions on graphics, 35(4), 1-10. https://doi.org/10.1145/2897824.2925936
In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preservin...
Real-Time Multi-View Facial Capture with Synthetic Training
Journal Article
Klaudiny, M., McDonagh, S., Bradley, D., Beeler, T., & Mitchell, K. (2017)
Real-Time Multi-View Facial Capture with Synthetic Training. Computer Graphics Forum, 36(2), 325-336. https://doi.org/10.1111/cgf.13129
We present a real-time multi-view facial capture system facilitated by synthetic training imagery. Our method is able to achieve high-quality markerless facial performance cap...
Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings
Journal Article
Bitterli, B., Rousselle, F., Moon, B., Iglesias-Guitián, J. A., Adler, D., Mitchell, K., …Novák, J. (2016)
Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings. Computer Graphics Forum, 35(4), 107-117. https://doi.org/10.1111/cgf.12954
We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide ran...