From left: Gary, Ian, Mark and Kurt
Today, Ian Doidge successfully defended his PhD thesis: Utilising Path-Vertex Data to Improve Monte Carlo Global Illumination.
Well done Ian. Mark W. Jones was the supervisor, Markus Roggenbach the viva chair, Gary Tam the internal examiner and Kurt Debattista (Warwick) the external.
Ian’s contributions were published as Probabilistic illumination-aware filtering for Monte Carlo rendering and Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination.
Video search interface
Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatio-temporal attributes from sports video to identify key instances of the team and player performance.
Phil A. Legg, David H. S. Chung, Matt L. Parry, Rhodri Bown, Mark W. Jones, Iwan W. Griffiths, Min Chen.
IEEE Transactions on Visualization and Computer Graphics, 19(12), 2109-2118.
We are just setting up our new biometric lab with 3D/4D cameras from 3dMD, Point Grey Gazelle high speed cameras, Kinect and Canon EOS cameras. The equipment will be primarily used for 3D and 4D reconstruction experiments by Jason Xie and Gary Tam.
Today we took delivery of our new Xeon Phi machine. We have 4 Xeon Phis and 2 Xeon 12 core CPUs making 260 cores in this single PC chassis. Ben Mora will supervise 2 new PhD students working on Physics problems and techniques with this machine. Mark W. Jones will also have a new PhD student that will be able to exploit this machine for ray tracing. Generally the machine will be used throughout the group as a platform for parallel computation using the new Intel MIC architecture.
This equipment was funded by the Welsh Government through RIVIC.
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.
We are pleased to announce our award of Best Paper at Eurographics 2013 for our paper Photon Parameterisation for Robust Relaxation Constraints, Ben Spencer and Mark W. Jones. [Link to paper]
The paper introduces a technique that augments each photon with information about its origin trajectory. Using this, lighting is separable during density estimation queries. Additionally, we spot fine edge detail using PCA allowing us to employ photon relaxation without detrimental effects. This results in high qualify photon maps that reduce variance and can be rendered with very low bandwidth kernels reducing bias. [Link to news on EG.org]
This paper presents a novel approach to detecting and preserving fine illumination structure within photon maps. Data derived from each photon’s primal trajectory is encoded and used to build a high-dimensional kd-tree. Incorporation of these new parameters allows for precise differentiation between intersecting ray envelopes, thus minimizing detail degradation when combined with photon relaxation. We demonstrate how parameter-aware querying is beneficial in both detecting and removing noise. We also propose a more robust structure descriptor based on principal components analysis that better identifies anisotropic detail at the sub-kernel level.We illustrate the effectiveness of our approach in several example scenes and show significant improvements when rendering complex caustics compared to previous methods.
Ben Spencer and Mark W. Jones
Computer Graphics Forum, Volume 32, Issue 2pt1, pages 83–92, May 2013. [doi]
Best paper, Eurographics 2013.
Researchers from Swansea, Cardiff, Aberystwyth and Bangor all attended the 2013 RIVIC graduate school in Bangor and Portmeirion 10th-11th April. Portmeirion was a great setting for the conference – we took over all of the village and some of the castle. We had lots of great talks from PhD students, researchers, lecturers and guest speakers of Hans-Peter Seidel and Min Chen.
Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing, and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g., deferred shading, isosurface extraction, and surface voxelization in computer graphics and visualization. We present a novel In-Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray-tracing-based visualization of volumetric data. We demonstrate that the proposed In-Kernel Compaction outperforms the standard out-of-kernel Thrust parallel-scan method for performing stream compaction in this real-world application. For the data visualization, we also propose a novel multi-kernel ray-tracing pipeline for increased thread coherency and show that it outperforms a conventional single-kernel approach.
D. M. Hughes, I. S. Lim, M. W. Jones, A. Knoll and B. Spencer
Computer Graphics Forum, 2013, 32(6), 178-188. [doi]