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.

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.

Coherent Photon Mapping on the Intel MIC Architecture

Photon mapping is a global illumination algorithm which is composed of two steps： photon tracing and photon searching. During photon searching step, each shading point needs to search the photon-tree to find k-neighbouring photons for reflected radiance estimation. As the number of shading points and the size of photon-tree are dramatically large, the photon searching step is time consuming. We propose a parallel photon searching algorithm by using radiance estimation approach for coherent shading points on the Intel Many Integrated Core （MIC） Architecture. In order to efficiently use single instruction multiple data （SIMD） units, shading points are clustered by similarity first （every cluster contains 16 shading-points）, and an initial neighbouring scope is searched from the photon-tree for each cluster. Then we use 16-wide SIMD units by performing k-NN searching from the initial neighbouring scope for those 16 shading-points in a cluster in parallel. We use the method to simulate some global illumination scenes on Intel Xeon processors and Intel Xeon Phi^TM coprocessors. The comparison results with existing photon mapping techniques indicate that our method gives significant improvement in speed with the same accuracy.

Anisotropic density estimation for photon mapping

Photon mapping is a widely used technique for global illumination rendering. In the density estimation step of photon mapping, the indirect radiance at a shading point is estimated through a filtering process using nearby stored photons; an isotropic filtering kernel is usually used. However,using an isotropic kernel is not always the optimal choice, especially for cases when eye paths intersect with surfaces with anisotropic BRDFs. In this paper,we propose an anisotropic filtering kernel for density estimation to handle such anisotropic eye paths.The anisotropic filtering kernel is derived from the recently introduced anisotropic spherical Gaussian representation of BRDFs. Compared to conventional photon mapping, our method is able to reduce rendering errors with negligible additional cost when rendering scenes containing anisotropic BRDFs.

Irradiance regression for efficient final gathering in global illumination

Photon mapping is widely used for global illumi- nation rendering because of its high computational efficiency. But its efficiency is still limited, mainly by the intensive sam- piing required in final gathering, a process that is critical for removing low frequency artifacts of density estimation. In this paper, we propose a method to predict the final gather- ing estimation with direct density estimation, thereby achiev- ing high quality global illumination by photon mapping with high efficiency. We first sample the irradiance of a subset of shading points by both final gathering and direct radiance es- timation. Then we use the samples as a training set to predict the final gathered irradiance of other shading points through regression. Consequently, we are able to achieve about three times overall speedup compared with straightforward final gathering in global illumination computation with the same rendering quality.

Volumetric Vector-Based Representation for Indirect Illumination Caching

This paper introduces a caching technique based on a volumetric representation that captures low-frequency indirect illumination.This structure is intended for efficient storage and manipulation of illumination.It is based on a 3D grid that stores a fixed set of irradiance vectors.During preprocessing,this representation can be built using almost any existing global illumination software.During rendering,the indirect illumination within a voxel is interpolated from its associated irradiance vectors,and is used as additional local light sources.Compared with other techniques,the 3D vector-based representation of our technique offers increased robustness against local geometric variations of a scene.We thus demonstrate that it may be employed as an efficient and high-quality caching data structure for bidirectional rendering techniques such as particle tracing or photon mapping.

GPU-BASED FLUID SIMULATION WITH FAST COLLISION DETECTION ON BOUNDARIES

In this paper,we present a method for fluid simulation based on smoothed particle hydrodynamic(SPH)with fast collision detection on boundaries on GPU.The major goal of our algorithm is to get a fast SPH simulation and rendering on GPU.Additionally,our algorithm has the following three features:At first,to make the SPH method GPU-friendly,we introduce a spatial hash method for neighbor search.After sorting the particles based on their grid index,neighbor search can be done quickly on GPU.Second,we propose a fast particle-boundary collision detection method.By precomputing the distance field of scene boundaries,collision detection’s computing cost arrived as O(n),which is much faster than the traditional way.Third,we propose a pipeline with fine-detail surface reconstruction,and progressive photon mapping working on GPU.We experiment our algorithm on different situations and particle numbers of scenes,and find out that our method gets good results.Our experimental data shows that we can simulate 100K particles,and up to 1000K particles scene at a rate of approximately 2 times per second.