Path traced, 16 samples using Probabilistic illumination-aware filtering
Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we present a probabilistic image based noise removal and irradiance filtering framework that preserves this high frequency detail such as hard shadows and glossy reflections, and imposes no restrictions on the characteristics of the light transport or materials. We maintain per-pixel clusters of the path traced samples and, using statistics from these clusters, derive an illumination aware filtering scheme based on the discrete Poisson probability distribution. Furthermore, we filter the incident radiance of the samples, allowing us to preserve and filter across high frequency and complex textures without limiting the effectiveness of the filter.
Ian C. Doidge and Mark W. Jones.
CGI 2013, The Visual Computer 29(6-8),707-616, 2013. The final publication is available at www.springerlink.com.
The Expectation Maximization (EM) algorithm is an alternative reconstruction method to the Filtered Back Projection method, providing many advantages including decreased sensitivity to noise. However the algorithm requires a large number of iterations to reach adequate convergence. Due to this, research has been carried out into accelerating the convergence rate of the EM algorithm. In this paper we present an analysis of an EM implementation which uses both OSEM and MGEM, comparing results on a per time basis with both acceleration techniques alone as well as a combination of the two methods. We provide an alternative stopping criterion based on the RMS error of the projections of the current reconstruction and compare the result with an existing variance based approach.
Andrew Ryan, Benjamin Mora and Min Chen.
IEEE International Conference on Image Processing 2012.
Parallel coordinates is one of the most popular and widely used visualization techniques for large, high dimensional data. Often, data attributes are visualized on individual axes with polylines joining them. However, some data attributes are more naturally represented with a spherical coordinate system. We present a novel coupling of parallel coordinates with spherical coordinates, enabling the visualization of vector and multi-dimensional data. The spherical plot is integrated as if it is an axis in the parallel coordinate visualization. This hybrid visualization benefits from enhanced visual perception, representing vector data in a more natural spatial domain and also reducing the number of parallel axis within the parallel coordinates plot. This raises several challenges which we discuss and provide solutions to, such as, visual clutter caused by over plotting and the computational complexity of visualizing large abstract, time-dependent data. We demonstrate the results of our work-in-progress visualization technique using biological animal tracking data of a large, multi-dimensional, time-dependent nature, consisting of tri-axial accelerometry samples as well as several additional attributes. In order to understand marine wildlife behavior, the acceleration vector is reconstructed in spherical coordinates and visualized alongside with the other data attributes to enable exploration, analysis and presentation of marine wildlife behavior.
James Walker, Zhao Geng, Mark W. Jones, Robert S. Laramee.
Eurovis Short Papers, 43-47, 2012. [doi] [BibTex]
A new rendering method that ray traces an entire row of the image at a time is introduced. This moves some of the ray tracing computations into a simplified 1D domain and reduces the memory requirements considerably. Visibility determination is performed efficiently using Hierarchical Occlusion Maps and provides faster renderings than packet ray tracing in general and OpenGL for large scenes. In addition, the algorithm shows near perfect scaling when multi-threaded and works very well with kd-trees and octrees, as implementations demonstrate. Finally, optimal rendering times are reached with trees that are an order of magnitude smaller than those required for regular ray tracing.
Ravi Prakash Kammaje and Benjamin Mora.
Proceedings of IEEE symposium on Interactive Ray-Tracing 2008, pp. 27-34.