Monte Carlo yolak izlenmiş düşük çözünürlüklü kaplamaların gürültüden arındırılması ve güdümlü yukarı örneklenmesi

Monte Carlo path tracing is used to generate renderings by estimating the rendering equation using the Monte Carlo method. An extensive amount of ray samples per pixel is needed to be cast during this rendering process to create an image with a low enough variance to be considered visually noise-free. Casting that amount of samples requires an expensive time budget. Many studies focus on rendering a noisy image at the original resolution with a decreased sample count and then applying a post-process denoising to produce a visually appealing output. This approach speeds up the rendering process and creates a denoised image of comparable quality to the visually noise-free ground truth. However, the denoising process cannot handle the noisy image’s high variance accurately if the sample count is decreased harshly to complete the rendering process in a shorter time budget. In this thesis work, we try to overcome this problem by proposing a pipeline that renders the image at a reduced resolution to cast more samples than the harshly decreased sample count in the same time budget. This noisy low-resolution image is then denoised more accurately, thanks to having a lower variance. It is then upsampled with the guidance of the auxiliary scene data rendered swiftly in a separate rendering pass at the original resolution. Experimental evaluation shows that the proposed pipeline generates denoised and guided upsampled images in promisingly good quality compared to denoising the noisy original resolution images rendered with the harshly decreased sample count.----M.S. - Master of Scienc

Efficient From-Point Visibility for Global Illumination in Virtual Scenes with Participating Media

Sichtbarkeitsbestimmung ist einer der fundamentalen Bausteine fotorealistischer Bildsynthese. Da die Berechnung der Sichtbarkeit allerdings äußerst kostspielig zu berechnen ist, wird nahezu die gesamte Berechnungszeit darauf verwendet. In dieser Arbeit stellen wir neue Methoden zur Speicherung, Berechnung und Approximation von Sichtbarkeit in Szenen mit streuenden Medien vor, die die Berechnung erheblich beschleunigen, dabei trotzdem qualitativ hochwertige und artefaktfreie Ergebnisse liefern

Instant global illumination on the GPU using OptiX

OptiX, a programmable ray tracing engine, has been recently made available by NVidia, relieving rendering researchers from the idiosyncrasies of efficient ray tracing programming and allowing them to concentrate on higher level algorithms, such as interactive global illumination. This paper evaluates the performance of the Instant Global Illumination algorithm on OptiX as well as the impact of three di fferent optimization techniques: imperfect visibility, downsampling and interleaved sampling. Results show that interactive frame rates are indeed achievable, although the combination of all optimization techniques leads to the appearance of artifacts that compromise image quality. Suggestions are presented on possible ways to overcome these limitations

Toward Efficient Rendering: A Neural Network Approach

Physically-based image synthesis has attracted considerable attention due to its wide applications in visual effects, video games, design visualization, and simulation. However, obtaining visually satisfactory renderings with ray tracing algorithms often requires casting a large number of rays and thus takes a vast amount of computation. The extensive computational and memory requirements of ray tracing methods pose a challenge, especially when running these rendering algorithms on resource-constrained platforms, and impede their applications that require high resolutions and refresh rates. This thesis presents three methods to address the challenge of efficient rendering. First, we present a hybrid rendering method to speed up Monte Carlo rendering algorithms. Our method first generates two versions of a rendering: one at a low resolution with a high sample rate (LRHS) and the other at a high resolution with a low sample rate (HRLS). We then develop a deep convolutional neural network to fuse these two renderings into a high-quality image as if it were rendered at a high resolution with a high sample rate. Specifically, we formulate this fusion task as a super-resolution problem that generates a high-resolution rendering from a low-resolution input (LRHS), assisted with the HRLS rendering. The HRLS rendering provides critical high-frequency details which are difficult to recover from the LRHS for any super-resolution methods. Our experiments show that our hybrid rendering algorithm is significantly faster than the state-of-the-art Monte Carlo denoising methods while rendering high-quality images when tested on both our own BCR dataset and the Gharbi dataset. Second, we investigate super-resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made in super-resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary features to guide the super-resolution of low-resolution renderings. These high-resolution auxiliary features can be quickly rendered by a rendering engine and, at the same time, provide valuable high-frequency details to assist super-resolution. To this end, we develop a cross-modality Transformer network that consists of an auxiliary feature branch and a low-resolution rendering branch. These two branches are designed to fuse high-resolution auxiliary features with the corresponding low-resolution rendering. Furthermore, we design residual densely-connected Swin Transformer groups for learning to extract representative features to enable high-quality super-resolution. Our experiments show that our auxiliary features-guided super-resolution method outperforms both state-of-the-art super-resolution methods and Monte Carlo denoising methods in producing high-quality renderings. Third, we present a deep-learning-based Monte Carlo Denoising method for the stereoscopic images. Research on deep-learning-based Monte Carlo denoising has made significant progress in recent years. However, existing methods are mostly designed for single-image Monte Carlo denoising, and stereoscopic image Monte Carlo denoising is less explored. Traditional methods require first rendering a noiseless for one view, which is time-consuming. Recent deep-learning-based methods achieve promising results on single-image Monte Carlo denoising, but their performance on the stereoscopic image is compromised as they do not consider the spatial correspondence between the left image and the right image. In this thesis, we present a deep-learning-based Monte Carlo denoising method for stereoscopic images. It takes low sampling per pixel (spp) stereoscopic images as inputs and estimates the high-quality result. Specifically, we extract features from two stereoscopic images and warp the features from one image to the other using the disparity finetuned from the disparity calculated from geometry. To train our network, we collected a large-scale Blender Cycles Stereo Ray-tracing dataset. Our experiments show that our method outperforms state-of-the-art methods when the sampling rates are low

Real-Time Hair Filtering with Convolutional Neural Networks

Rendering of realistic-looking hair is in general still too costly to do in real-time applications, from simulating the physics to rendering the fine details required for it to look natural, including self-shadowing.We show how an autoencoder network, that can be evaluated in real time, can be trained to filter an image of few stochastic samples, including self-shadowing, to produce a much more detailed image that takes into account real hair thickness and transparency

Neural Microfacet Fields for Inverse Rendering

We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance model within a volumetric setting by treating each sample along the ray as a (potentially non-opaque) surface. Using surface-based Monte Carlo rendering in a volumetric setting enables our method to perform inverse rendering efficiently by combining decades of research in surface-based light transport with recent advances in volume rendering for view synthesis. Our approach outperforms prior work in inverse rendering, capturing high fidelity geometry and high frequency illumination details; its novel view synthesis results are on par with state-of-the-art methods that do not recover illumination or materials.Comment: Project page: https://half-potato.gitlab.io/posts/nmf