International audienceTone Mapping Operators (TMOs) aim at converting real world high dynamic range (HDR) images captured withHDR cameras, into low dynamic range (LDR) images that can be displayed on LDR displays. Several TMOshave been proposed over the last decade, from the simple global mapping to the more complex one simulating thehuman vision system. While these solutions work generally well for still pictures, they are usually less efficient forvideo sequences as they are source of visual artifacts. Only few of them can be adapted to cope with a sequenceof images. In this paper we present a major problem that a static TMO usually encounters while dealing withvideo sequences, namely the temporal coherency. Indeed, as each tone mapper deals with each frame separately,no temporal coherency is taken into account and hence the results can be quite disturbing for high varyingdynamics in a video. We propose a temporal coherency algorithm that is designed to analyze a video as a whole,and from its characteristics adapts each tone mapped frame of a sequence in order to preserve the temporalcoherency. This temporal coherency algorithm has been tested on a set of real as well as Computer GraphicsImage (CGI) content and put in competition with several algorithms that are designed to be time-dependent.Results show that temporal coherency preserves the overall contrast in a sequence of images. Furthermore, thistechnique is applicable to any TMO as it is a post-processing that only depends on the used TMO
Multiple importance sampling (MIS) is a provably good way to combine a finite set of sampling techniques to reduce variance in Monte Carlo integral estimation. However, there exist integration problems for which a continuum of sampling techniques is available. To handle such cases we establish a continuous MIS (CMIS) formulation as a generalization of MIS to uncountably infinite sets of techniques. Our formulation is equipped with a base estimator that is coupled with a provably optimal balance heuristic and a practical stochastic MIS (SMIS) estimator that makes CMIS accessible to a broad range of problems. To illustrate the effectiveness and utility of our framework, we apply it to three different light transport applications, showing improved performance over the prior state-of-the-art techniques.
Fig. 1. Equal-time (5 minutes) renderings of a smoky Kitchen scene. Gradient-domain volumetric rendering techniques with L1 reconstruction converge faster than primal-domain volumetric rendering technique. The relMSE error metric has a unitless scale of 10 −2 .Gradient-domain rendering can improve the convergence of surface-based light transport by exploiting smoothness in image space. Scenes with participating media exhibit similar smoothness and could potentially benefit from gradient-domain techniques. We introduce the first gradient-domain formulation of image synthesis with homogeneous participating media, including four novel and efficient gradient-domain volumetric density estimation algorithms. We show that naïve extensions of gradient domain path-space and density estimation methods to volumetric media, while functional, can result in inefficient estimators. Focussing on point-, beam-and plane-based gradient-domain estimators, we introduce a novel shift mapping that eliminates redundancies in the naïve formulations using spatial relaxation within the volume. We show that gradient-domain volumetric rendering improve convergence compared to primal domain state-of-the-art, across a suite of scenes. Our formulation and algorithms support progressive estimation and are easy to incorporate atop existing renderers.Authors' addresses: Adrien Gruson, adrien
Layered materials capture subtle, realistic reflection behaviors that traditional single‐layer models lack. Much of this is due to the complex subsurface light transport at the interfaces of – and in the media between – layers. Rendering with these materials can be costly, since we must simulate these transport effects at every evaluation of the underlying reflectance model. Rendering an image requires thousands of such evaluations, per pixel. Recent work treats this complexity by introducing significant approximations, requiring large precomputed datasetsper material, or simplifying the light transport simulations within the materials. Even the most effective of these methods struggle with the complexity induced by high‐frequency variation in reflectance parameters and micro‐surface normal variation, as well as anisotropic volumetric scattering between the layer interfaces. We present a more efficient, unbiased estimator for light transport in such general, complex layered appearance models. By conducting an analysis of the types of transport paths that contribute most to the aggregate reflectance dynamics, we propose an effective and unbiased path sampling method that reduces variance in the reflectance evaluations. Our method additionally supports reflectance importance sampling, does not rely on any precomputation, and so integrates readily into existing renderers. We consistently outperform the state‐of‐the‐art by ~2 – 6× in equal‐quality (i.e., equal error) comparisons.
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