35-Issue 4

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/15440

Browse

Browsing 35-Issue 4 by Subject "Raytracing"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Improving the Dwivedi Sampling Scheme
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Meng, Johannes; Hanika, Johannes; Dachsbacher, Carsten; Elmar Eisemann and Eugene Fiume
    Despite recent advances in Monte Carlo rendering techniques, dense, high-albedo participating media such as wax or skin still remain a difficult problem. In such media, random walks tend to become very long, but may still lead to a large contribution to the image. The Dwivedi sampling scheme, which is based on zero variance random walks, biases the sampling probability distributions to exit the medium as quickly as possible. This can reduce variance considerably under the assumption of a locally homogeneous medium with constant phase function. Prior work uses the normal at the Point of Entry as the bias direction. We demonstrate that this technique can fail in common scenarios such as thin geometry with a strong backlight. We propose two new biasing strategies, Closest Point and Incident Illumination biasing, and show that these techniques can speed up convergence by up to an order of magnitude. Additionally, we propose a heuristic approach for combining biased and classical sampling techniques using Multiple Importance Sampling.
  • No Thumbnail Available
    Item
    Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Bitterli, Benedikt; Rousselle, Fabrice; Moon, Bochang; Iglesias-Guitian, Jose A.; Adler, David; Mitchell, Kenny; Jarosz, Wojciech; Novak, Jan; Elmar Eisemann and Eugene Fiume
    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 range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limitations of their corresponding components with an emphasis on production requirements. The observations of our analysis instruct the design of our new filter that offers high-quality results and stable performance. A key observation of our analysis is that using auxiliary buffers (normal, albedo, etc.) to compute the regression weights greatly improves the robustness of zero-order models, but can be detrimental to first-order models. Consequently, our filter performs a first-order regression leveraging a rich set of auxiliary buffers only when fitting the data, and, unlike recent works, considers the pixel color alone when computing the regression weights. We further improve the quality of our output by using a collaborative denoising scheme. Lastly, we introduce a general mean squared error estimator, which can handle the collaborative nature of our filter and its nonlinear weights, to automatically set the bandwidth of our regression kernel.
  • No Thumbnail Available
    Item
    Solid Angle Sampling of Disk and Cylinder Lights
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Gamito, Manuel N.; Elmar Eisemann and Eugene Fiume
    A new unbiased sampling approach is presented, which allows the direct illumination from disk and cylinder light sources to be sampled with a uniform probability distribution within their solid angles, as seen from each illuminated point. This approach applies to any form of global illumination path tracing algorithm (forward or bidirectional), where the direct illumination integral from light sources needs to be estimated. We show that taking samples based on the solid angle of these two light sources leads to improved estimates and reduced variance of the Monte Carlo integral for direct illumination. This work follows from previously known unbiased methods for the solid angle sampling of triangular and rectangular light sources and extends the class of lights that can be rendered with these improved sampling algorithms.
  • No Thumbnail Available
    Item
    Sparse High-degree Polynomials for Wide-angle Lenses
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Schrade, Emanuel; Hanika, Johannes; Dachsbacher, Carsten; Elmar Eisemann and Eugene Fiume
    Rendering with accurate camera models greatly increases realism and improves the match of synthetic imagery to real-life footage. Photographic lenses can be simulated by ray tracing, but the performance depends on the complexity of the lens system, and some operations required for modern algorithms, such as deterministic connections, can be difficult to achieve. We generalise the approach of polynomial optics, i.e. expressing the light field transformation from the sensor to the outer pupil using a polynomial, to work with extreme wide angle (fisheye) lenses and aspherical elements. We also show how sparse polynomials can be constructed from the large space of high-degree terms (we tested up to degree 15). We achieve this using a variant of orthogonal matching pursuit instead of a Taylor series when computing the polynomials. We show two applications: photorealistic rendering using Monte Carlo methods, where we introduce a new aperture sampling technique that is suitable for light tracing, and an interactive preview method suitable for rendering with deep images.