Importance driven environment map sampling

In this paper we present an automatic and efficient method for supporting Image Based Lighting (IBL) for bidirectional methods which improves both the sampling of the environment, and the detection and sampling of important regions of the scene, such as windows and doors. These often have a small area proportional to that of the entire scene, so paths which pass through them are generated with a low probability. The method proposed in this paper improves this by taking into account view importance, and modifies the lighting distribution to use light transport information. This also automatically constructs a sampling distribution in locations which are relevant to the camera position, thereby improving sampling. Results are presented when our method is applied to bidirectional rendering techniques, in particular we show results for Bidirectional Path Tracing, Metropolis Light Transport and Progressive Photon Mapping. Efficiency results demonstrate speed up of orders of magnitude (depending on the rendering method used), when compared to other methods

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Exploring heterogeneous computing with advanced path tracing algorithms

The CG research community has a renewed interest on rendering algorithms based on path space integration, mainly due to new approaches to discover, generate and exploit relevant light paths while keeping the numerical integrator unbiased or, at the very least, consistent. Simultaneously, the current trend towards massive parallelism and heterogeneous environments, based on a mix of conventional computing units with accelerators, is playing a major role both in HPC and embedded platforms. To efficiently use the available resources in these and future systems, algorithms and software packages are being revisited and reevaluated to assess their adequateness to these environments. This paper assesses the performance and scalability of three different path based algorithms running on homogeneous servers (dual multicore Xeons) and heterogeneous systems (those multicore plus manycore Xeon and NVidia Kepler GPU devices). These algorithms include path tracing (PT), its bidirectional counterpart (BPT) and the more recent Vertex Connect and Merge (VCM). Experimental results with two conventional scenes (one mainly diffuse, the other exhibiting specular-diffuse-specular paths) show that all algorithms scale well across the different platforms, the actual scalability depending on whether shared data structures are accessed or not (PT vs. BPT vs. VCM).This work was supported by COMPETE: POCI-01-0145FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by the Cooperation Program with the University of Texas at Austin and co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund

Autonomous Lighting Agents in Photon Mapping

proceedings of ISVC'05International audienceIn computer graphics, global illumination algorithms such as photon mapping require to gather large volumes of data which can be heavily redundant.We propose both a new characterization of useful data and a new optimization method for the photon mapping algorithm using structures borrowed from Artificial Intelligence such as autonomous agents. Our autonomous lighting agents efficiently gather large amounts of useful data and are used to make decisions during rendering. It induces less photons being cast and shorter rendering times in both photon casting and rendering phase of the photon mapping algorithm which leads to an important decrease of memory occupation and slightly shorter rendering times for equal image quality

Characterizing the parallax error in multi-pinhole micro-SPECT reconstruction

The usage of pinholes is very important in preclinical micro-SPECT. Pinholes can magnify the object onto the detector, resulting in better system resolutions than the detector resolution. The loss in sensitivity is usually countered by adding more pinholes, each projecting onto a specific part of the detector. As a result, gamma rays have an oblique incidence to the detector. This causes displacement and increased uncertainty in the position of the interaction of the gamma ray in the detector, also known as parallax errors or depth-of-interaction (DOI) errors. This in turn has a large influence on image reconstruction algorithms using ray tracers as a forward projector model, as the end-point of each ray on the detector has to be accurately known. In this work, we used GATE to simulate the FLEX Triumph-I system (Gamma Medica-Ideas, Northridge, CA), a CZT-based multi-pinhole micro-SPECT system. This system uses 5 mm thick CZT pixels, with 1.5 mm pixel pitch. The simulated information was then used to enhance the image resolution by accurately modeling the DOI. Two hundred point sources were simulated and rebinned to use the DOI information. This data was then used in a GPU-based iterative reconstruction algorithm taking the simulated DOI into account. The average displacement was then determined for all point sources, and the FWHM was calculated in three dimensions, by fitting the point sources with 3D Gaussians. We show that the displacement is reduced by 83% on average. We also show a 15% resolution gain when only 5 DOI levels are used