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Ondrej Karlik

dblp:140/1122 · also Ondrej Karlík · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 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
3 papers
Rendering · 93% Visualization and visual analytics · 7%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

Topics — the 10 heaviest of 11, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering
global illumination
0.622019
MIS compensation: optimizing sampling techniques in multiple importance sampling · ACM Trans. Graph. 2019
Toward Evaluating the Usefulness of GlobalIllumination for Novices in Lighting Design Tasks · IEEE Trans. Vis. Comput. Graph. 2014
Rendering
monte carlo rendering
0.622019
MIS compensation: optimizing sampling techniques in multiple importance sampling · ACM Trans. Graph. 2019
On-line learning of parametric mixture models for light transport simulation · ACM Trans. Graph. 2014
Rendering › sampling
multiple importance sampling
0.412019
MIS compensation: optimizing sampling techniques in multiple importance sampling · ACM Trans. Graph. 2019
Rendering › light transport
path guiding
0.412019
MIS compensation: optimizing sampling techniques in multiple importance sampling · ACM Trans. Graph. 2019
Rendering › monte carlo rendering
importance sampling
0.212014
On-line learning of parametric mixture models for light transport simulation · ACM Trans. Graph. 2014
Rendering › lighting
lighting design
0.212014
Toward Evaluating the Usefulness of GlobalIllumination for Novices in Lighting Design Tasks · IEEE Trans. Vis. Comput. Graph. 2014
Rendering
light transport
0.212014
On-line learning of parametric mixture models for light transport simulation · ACM Trans. Graph. 2014
Visualization and visual analytics › visualization evaluation
user study
0.212014
Toward Evaluating the Usefulness of GlobalIllumination for Novices in Lighting Design Tasks · IEEE Trans. Vis. Comput. Graph. 2014
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model
mixture model
0.112014
On-line learning of parametric mixture models for light transport simulation · ACM Trans. Graph. 2014
Rendering › light transport
direct lighting
0.112014
Toward Evaluating the Usefulness of GlobalIllumination for Novices in Lighting Design Tasks · IEEE Trans. Vis. Comput. Graph. 2014

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

variance reduction · 0.4sampling density optimization · 0.4particle-based estimation · 0.4online learning · 0.4user study · 0.2parametric mixture models · 0.2parametric mixture model · 0.2
YearPublicationVenuePosition
2019 MIS compensation: optimizing sampling techniques in multiple importance sampling
abstract
Multiple importance sampling (MIS) has become an indispensable tool in Monte Carlo rendering, widely accepted as a near-optimal solution for combining different sampling techniques. But an MIS combination, using the common balance or power heuristics, often results in an overly defensive estimator, leading to high variance. We show that by generalizing the MIS framework, variance can be substantially reduced. Specifically, we optimize one of the combined sampling techniques so as to decrease the overall variance of the resulting MIS estimator. We apply the approach to the computation of direct illumination due to an HDR environment map and to the computation of global illumination using a path guiding algorithm. The implementation can be as simple as subtracting a constant value from the tabulated sampling density done entirely in a preprocessing step. This produces a consistent noise reduction in all our tests with no negative influence on run time, no artifacts or bias, and no failure cases.
Ondrej Karlik, Martin Sik, Petr Vévoda, Tomás Skrivan, Jaroslav Krivánek
ACM Trans. Graph.1
2014 On-line learning of parametric mixture models for light transport simulation
abstract
Monte Carlo techniques for light transport simulation rely on importance sampling when constructing light transport paths. Previous work has shown that suitable sampling distributions can be recovered from particles distributed in the scene prior to rendering. We propose to represent the distributions by a parametric mixture model trained in an on-line (i.e. progressive) manner from a potentially infinite stream of particles. This enables recovering good sampling distributions in scenes with complex lighting, where the necessary number of particles may exceed available memory. Using these distributions for sampling scattering directions and light emission significantly improves the performance of state-of-the-art light transport simulation algorithms when dealing with complex lighting.
Jirí Vorba, Ondrej Karlik, Martin Sik, Tobias Ritschel 0001, Jaroslav Krivánek
ACM Trans. Graph.2
2014 Toward Evaluating the Usefulness of GlobalIllumination for Novices in Lighting Design Tasks
abstract
Thanks to its ability to improve the realism of computer-generated imagery, the use of global illumination has recently become widespread among digital lighting artists. It remains unclear, though, what impact it has on the lighting design workflows, especially for novice users. In this paper we present a user study which investigates the use of global illumination, large area lights, and non-physical fill lights in lighting design tasks, where 26 novice subjects design lighting with these tools. The collected data suggest that global illumination is not significantly harder to control for novice users that direct illumination, and when given the possibility, most users opt to use it in their designs. The use of global illumination together with large area lights leads to simpler lighting setups with fewer non-physical fill lights. Interestingly, global illumination does not supersede fill lights: users still include them into their globally illuminated lighting setups. We believe that our results will find use in the development of lighting design tools for non-expert users.
Ondrej Karlik, Martin Ruzicka, Václav Gassenbauer, Fabio Pellacini, Jaroslav Krivánek
IEEE Trans. Vis. Comput. Graph.1