A Tiny Example Based Procedural Model for Real-Time Glinty Appearance Rendering

The glinty details from complex microstructures significantly enhance rendering realism.However,the previous methods use high-resolution normal maps to define each micro-geometry,which requires huge memory overhead.This paper observes that many self-similarity materials have independent structural characteristics,which we define as tiny example microstructures.We propose a procedural model to represent microstructures implicitly by performing spatial transformations and spatial distribution on tiny examples.Furthermore,we precompute normal distribution functions(NDFs)by 4D Gaussians for tiny examples and store them in multi-scale NDF maps.Combined with a tiny example based NDF evaluation method,complex glinty surfaces can be rendered simply by texture sampling.The experimental results show that our tiny example based the microstructure rendering method is GPU-friendly,successfully reproducing high-frequency reflection features of different microstructures in real time with low memory and computational overhead.

Unsupervised Reconstruction for Gradient-Domain Rendering with Illumination Separation

Gradient-domain rendering methods can render higher-quality images at the same time cost compared with traditional ray tracing rendering methods, and, combined with the neural network, achieve better rendering quality than conventional screened Poisson reconstruction. However, it is still challenging for these methods to keep detailed information, especially in areas with complex indirect illumination and shadows. We propose an unsupervised reconstruction method that separates the direct rendering from the indirect, and feeds them into our unsupervised network with some corresponding auxiliary channels as two separated tasks. In addition, we introduce attention modules into our network which can further improve details. We finally combine the results of the direct and indirect illumination tasks to form the rendering results. Experiments show that our method significantly improves image quality details, especially in scenes with complex conditions.

Denoising Stochastic Progressive Photon Mapping Renderings Using a Multi-Residual Network

Stochastic progressive photon mapping(SPPM)is one of the important global illumination methods in computer graphics.It can simulate caustics and specular-diffuse-specular lighting effects efficiently.However,as a biased method,it always suffers from both bias and variance with limited iterations,and the bias and the variance bring multi-scale noises into SPPM renderings.Recent learning-based methods have shown great advantages on denoising unbiased Monte Carlo(MC)methods,but have not been leveraged for biased ones.In this paper,we present the first learning-based method specially designed for denoising-biased SPPM renderings.Firstly,to avoid conflicting denoising constraints,the radiance of final images is decomposed into two components:caustic and global.These two components are then denoised separately via a two-network framework.In each network,we employ a novel multi-residual block with two sizes of filters,which significantly improves the model’s capabilities,and makes it more suitable for multi-scale noises on both low-frequency and high-frequency areas.We also present a series of photon-related auxiliary features,to better handle noises while preserving illumination details,especially caustics.Compared with other state-of-the-art learning-based denoising methods that we apply to this problem,our method shows a higher denoising quality,which could efficiently denoise multi-scale noises while keeping sharp illuminations.

A survey of photon mapping state-of-the-art research and future challenges

Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.

Adaptive Photon Mapping Based on Gradient

Photon mapping can simulate some special effects efficiently such as shadows and caustics. Photon mapping runs in two phases： the photon map generating phase and the radiance estimation phase. In this paper, we focus on the bandwidth selection process in the second phase, as it can affect the final quality significantly. Poor results with noise arise if few photons are collected, while bias appears if a large number of photons are collected. In order to solve this issue, we propose an adaptive radiance estimation solution to obtain trade-offs between noise and bias by changing the number of neighboring photons and the shape of the collected area according to the radiance gradient. Our approach can be applied in both the direct and the indirect illumination computation. Finally, experimental results show that our approach can produce smoother quality while keeping the high frequency features perfectly compared with the original photon mapping algorithm.