Photorealistic physically based render engines: a comparative study

Pérez Roig, F. (2012). Photorealistic physically based render engines: a comparative study. http://hdl.handle.net/10251/14797.Archivo delegad

Manipulação interativa de cenas fotorealistas usando path tracing

Rendering pleasing photorealistic images requires both a high-quality renderer and wellcrafted scenes. While rendering algorithms and systems have made some impressive progress over the last two decades, creating nice scenes still remains highly dependent of the artistic skills of the modeler. As a result, researchers tend to rely on a small number of existing good-looking scenes to test their algorithms. While creating new scenes from scratch is difficult for non-artists, editing existing scenes to achieve new and desired results is a task at the reach of the average graphics user. We present a system that allows users with no special artistic skills to create new scenes by modifying existing ones through a simple user interface. Enabled by modern hardware and software advancements, we render the scenes photorealistically using path tracing and provide instant feedback on the user modifications. The quality of the images generated by our system is comparable to established offline renderers, such as PBRT, while still maintaining interactive performance. Our system should stimulate the creation of new scene datasets, and allow anyone to customize existing scenes with shapes and materials according to his/her specific needs or desires. The easy customization of scenes and the high-quality renderings produced by our system may also stimulate other professionals, such as designers, scenographers, architects, decorators, etc. to make more intense use of computer generated imaging in their daily tasks.Renderizar imagens fotorealistas agradáveis requer tanto um renderizador de alta qualidade quanto cenas bem feitas. Enquanto sistemas e algoritmos de rendering tiveram progressos impressionantes nas últimas duas décadas, a criação de cenas interessantes ainda é altamente dependente nas habilidades artísticas do modelador. Como resultado, pesquisadores tendem a usar uma porção pequena de boas cenas já existentes para testar seus algoritmos. Embora a criação de cenas do zero seja difícil para não-artistas, editar cenas existentes para conseguir novos resultados é uma tarefa ao alcance do usuário médio de computação gráfica. Nós apresentamos um sistema que permite usuários sem habilidades artísticas especiais a criar novas cenas modificando cenas existentes através de uma interface simples. Baseado em avanços recentes em hardware e software nós renderizamos as cenas fotorealisticamente usando path tracing, provendo feedback instantâneo nas modificações do usuário. A qualidade das imagens geradas pelo nosso sistema é comparável a renderizadores offline estabelecidos, como o PBRT, enquanto ainda mantendo performance interativa. Nosso sistema deve estimular a criação de novos datasets de cenas, e permitir a qualquer um a customizar cenas existentes com formas e materiais de acordo com sua necessidade ou desejo. A fácil customização de cenas e as imagens de alta qualidade produzidas pelo nosso sistema também podem estimular outros profissionais, como designers, cenógrafos, arquitetus, decoradores, etc. a fazer uso mais intenso de imagens geradas por computador em suas tarefas diárias

Neural probabilistic path prediction : skipping paths for acceleration

La technique de tracé de chemins est la méthode Monte Carlo la plus populaire en infographie pour résoudre le problème de l'illumination globale. Une image produite par tracé de chemins est beaucoup plus photoréaliste que les méthodes standard tel que le rendu par rasterisation et même le lancer de rayons. Mais le tracé de chemins est coûteux et converge lentement, produisant une image bruitée lorsqu'elle n'est pas convergée. De nombreuses méthodes visant à accélérer le tracé de chemins ont été développées, mais chacune présente ses propres défauts et contraintes. Dans les dernières avancées en apprentissage profond, en particulier dans le domaine des modèles génératifs conditionnels, il a été démontré que ces modèles sont capables de bien apprendre, modéliser et tirer des échantillons à partir de distributions complexes. Comme le tracé de chemins dépend également d'un tel processus sur une distribution complexe, nous examinons les similarités entre ces deux problèmes et modélisons le processus de tracé de chemins comme un processus génératif. Ce processus peut ensuite être utilisé pour construire un estimateur efficace avec un réseau neuronal afin d'accélérer le temps de rendu sans trop d'hypothèses sur la scène. Nous montrons que notre estimateur neuronal (NPPP), utilisé avec le tracé de chemins, peut améliorer les temps de rendu d'une manière considérable sans beaucoup compromettre sur la qualité du rendu. Nous montrons également que l'estimateur est très flexible et permet à un utilisateur de contrôler et de prioriser la qualité ou le temps de rendu, sans autre modification ou entraînement du réseau neuronal.Path tracing is one of the most popular Monte Carlo methods used in computer graphics to solve the problem of global illumination. A path traced image is much more photorealistic compared to standard rendering methods such as rasterization and even ray tracing. Unfortunately, path tracing is expensive to compute and slow to converge, resulting in noisy images when unconverged. Many methods aimed to accelerate path tracing have been developed, but each has its own downsides and limitiations. Recent advances in deep learning, especially with conditional generative models, have shown to be very capable at learning, modeling, and sampling from complex distributions. As path tracing is also dependent on sampling from complex distributions, we investigate the similarities between the two problems and model the path tracing process itself as a conditional generative process. It can then be used to build an efficient neural estimator that allows us to accelerate rendering time with as few assumptions as possible. We show that our neural estimator (NPPP) used along with path tracing can improve rendering time by a considerable amount without compromising much in rendering quality. The estimator is also shown to be very flexible and allows a user to control and prioritize quality or rendering time, without any further training or modifications to the neural network

Rendering of light shaft and shadow for indoor environments enhancing technique

The ray marching methods have become the most attractive method to provide realism in rendering the effects of light scattering in the participating media of numerous applications. This has attracted significant attention from the scientific community. Up-sampling of ray marching methods is suitable to evaluate light scattering effects such as volumetric shadows and light shafts for rendering realistic scenes, but suffers of cost a lot for rendering. Therefore, some encouraging outcomes have been achieved by using down-sampling of ray marching approach to accelerate rendered scenes. However, these methods are inherently prone to artifacts, aliasing and incorrect boundaries due to the reduced number of sample points along view rays. This study proposed a new enhancing technique to render light shafts and shadows taking into consideration the integration light shafts, volumetric shadows, and shadows for indoor environments. This research has three major phases that cover species of the effects addressed in this thesis. The first phase includes the soft volumetric shadows creation technique called Soft Bilateral Filtering Volumetric Shadows (SoftBiF-VS). The soft shadow was created using a new algorithm called Soft Bilateral Filtering Shadow (SBFS). This technique was started by developing an algorithm called Imperfect Multi-View Soft Shadows (IMVSSs) based on down-sampling multiple point lights (DMPLs) and multiple depth maps, which are processed by using bilateral filtering to obtain soft shadows. Then, down-sampling light scattering model was used with (SBFS) to create volumetric shadows, which was improved using cross-bilateral filter to get soft volumetric shadows. In the second phase, soft light shaft was generated using a new technique called Realistic Real-Time Soft Bilateral Filtering Light Shafts (realTiSoftLS). This technique computed the light shaft depending on down-sampling volumetric light model and depth test, and was interpolated by bilateral filtering to gain soft light shafts. Finally, an enhancing technique for integrating all of these effects that represent the third phase of this research was achieved. The performance of the new enhanced technique was evaluated quantitatively and qualitatively a measured using standard dataset. Results from the experiment showed that 63% of the participants gave strong positive responses to this technique of improving realism. From the quantitative evaluation, the results revealed that the technique has dramatically outpaced the stateof- the-art techniques with a speed of 74 fps in improving the performance for indoor environments

Simulation-based Planning of Machine Vision Inspection Systems with an Application to Laser Triangulation

Nowadays, vision systems play a central role in industrial inspection. The experts typically choose the configuration of measurements in such systems empirically. For complex inspections, however, automatic inspection planning is essential. This book proposes a simulation-based approach towards inspection planning by contributing to all components of this problem: simulation, evaluation, and optimization. As an application, inspection of a complex cylinder head by laser triangulation is studied