Computer Graphics ForumComputer Graphics Forum (Print ISSN: 0167-7055; Online ISSN: 1467-8659)https://diglib.eg.org:443/handle/10.2312/12018-09-25T05:17:10Z2018-09-25T05:17:10ZData Reduction Techniques for Simulation, Visualization and Data AnalysisLi, S.Marsaglia, N.Garth, C.Woodring, J.Clyne, J.Childs, H.https://diglib.eg.org:443/handle/10.1111/cgf133362018-09-12T18:25:56Z2018-01-01T00:00:00ZData Reduction Techniques for Simulation, Visualization and Data Analysis
Li, S.; Marsaglia, N.; Garth, C.; Woodring, J.; Clyne, J.; Childs, H.
Chen, Min and Benes, Bedrich
Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.
2018-01-01T00:00:00ZRe‐Weighting Firefly Samples for Improved Finite‐Sample Monte Carlo EstimatesZirr, TobiasHanika, JohannesDachsbacher, Carstenhttps://diglib.eg.org:443/handle/10.1111/cgf133352018-09-12T18:25:56Z2018-01-01T00:00:00ZRe‐Weighting Firefly Samples for Improved Finite‐Sample Monte Carlo Estimates
Zirr, Tobias; Hanika, Johannes; Dachsbacher, Carsten
Chen, Min and Benes, Bedrich
Samples with high contribution but low probability density, often called fireflies, occur in all practical Monte Carlo estimators and are part of computing unbiased estimates. For finite‐sample estimates, however, they can lead to excessive variance. Rejecting all samples classified as outliers, as suggested in previous work, leads to estimates that are too low and can cause undesirable artefacts. In this paper, we show how samples can be re‐weighted depending on their contribution and sampling frequency such that the finite‐sample estimate gets closer to the correct expected value and the variance can be controlled. For this, we first derive a theory for how samples should ideally be re‐weighted and that this would require the probability density function of the optimal sampling strategy. As this probability density function is generally unknown, we show how the discrepancy between the optimal and the actual sampling strategy can be estimated and used for re‐weighting in practice. We describe an efficient algorithm that allows for the necessary analysis of per‐pixel sample distributions in the context of Monte Carlo rendering without storing any individual samples, with only minimal changes to the rendering algorithm. It causes negligible runtime overhead, works in constant memory and is well suited for parallel and progressive rendering. The re‐weighting runs as a fast post‐process, can be controlled interactively and our approach is non‐destructive in that the unbiased result can be reconstructed at any time.Samples with high contribution but low probability density, often called fireflies, occur in all practical Monte Carlo estimators and are part of computing unbiased estimates. For finite‐sample estimates, however, they can lead to excessive variance. Rejecting all samples classified as outliers, as suggested in previous work, leads to estimates that are too low and can cause undesirable artefacts. In this paper, we show how samples can be re‐weighted depending on their contribution and sampling frequency such that the finite‐sample estimate gets closer to the correct expected value and the variance can be controlled. For this, we first derive a theory for how samples should ideally be re‐weighted and that this would require the probability density function of the optimal sampling strategy. As this probability density function is generally unknown, we show how the discrepancy between the optimal and the actual sampling strategy can be estimated and used for re‐weighting in practice. We describe an efficient algorithm that allows for the necessary analysis of per‐pixel sample distributions in the context of Monte Carlo rendering without storing any individual samples, with only minimal changes to the rendering algorithm.
2018-01-01T00:00:00ZPencilArt: A Chromatic Penciling Style Generation FrameworkGao, ChengyingTang, MengyueLiang, XiangguoSu, ZhuoZou, Changqinghttps://diglib.eg.org:443/handle/10.1111/cgf133342018-09-12T18:25:55Z2018-01-01T00:00:00ZPencilArt: A Chromatic Penciling Style Generation Framework
Gao, Chengying; Tang, Mengyue; Liang, Xiangguo; Su, Zhuo; Zou, Changqing
Chen, Min and Benes, Bedrich
Non‐photorealistic rendering has been an active area of research for decades whereas few of them concentrate on rendering chromatic penciling style. In this paper, we present a framework named as PencilArt for the chromatic penciling style generation from wild photographs. The structural outline and textured map for composing the chromatic pencil drawing are generated, respectively. First, we take advantage of deep neural network to produce the structural outline with proper intensity variation and conciseness. Next, for the textured map, we follow the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings. Eventually, we evaluate PencilArt via a series of comparisons to previous work, showing that our results better capture the main features of real chromatic pencil drawings and have an improved visual appearance.Non‐photorealistic rendering has been an active area of research for decades whereas few of them concentrate on rendering chromatic penciling style. In this paper, we present a framework named as PencilArt for the chromatic penciling style generation from wild photographs. The structural outline and textured map for composing the chromatic pencil drawing are generated, respectively. First, we take advantage of deep neural network to produce the structural outline with proper intensity variation and conciseness. Next, for the textured map, we follow the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings. Eventually, we evaluate PencilArt via a series of comparisons to previous work, showing that our results better capture the main features of real chromatic pencil drawings and have an improved visual appearance.
2018-01-01T00:00:00ZData‐Driven Crowd Motion Control With Multi‐Touch GesturesShen, YijunHenry, JosephWang, HeHo, Edmond S. L.Komura, TakuShum, Hubert P. H.https://diglib.eg.org:443/handle/10.1111/cgf133332018-09-12T18:25:54Z2018-01-01T00:00:00ZData‐Driven Crowd Motion Control With Multi‐Touch Gestures
Shen, Yijun; Henry, Joseph; Wang, He; Ho, Edmond S. L.; Komura, Taku; Shum, Hubert P. H.
Chen, Min and Benes, Bedrich
Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model.
2018-01-01T00:00:00Z