基于自适应光子发射的渐进式光子映射

通过对渐进式光子映射算法进行扩展,提出了一种基于自适应光子发射的渐进式光子映射算法.渐进式光子映射是一个多遍的全局光照算法,通过不断发射光子并渐进更新场景各点的光能估计能使其最终能收敛到无偏差的结果.由于渐进式光子映射完全使用密度估计来计算各点的光能,因此其收敛速度受光子分布影响较大.利用渐进式光子映射算法中固有的场景统计信息以及其多遍的特点,设计了一个自适应的光子发射策略,使得发射的光子能更多的分布在对最终绘制有效的区域,提高了原算法的绘制效率.     

使用光子映射渲染参与介质

光子映射是近年发展起来的一种新的全局光照算法。本文依据光子映射对实体物体的渲染,将其扩展到对包含参与介质的场景的渲染,为此提出了一个两路的渲染算法。在第一路中,光子从光源发射,并使用光子追踪来构造体光子图;第二路从视点出发向场景中发射光线,使用光线追踪来进行渲染,其中,根据构造好的光子图,用光线步进进行
行递归的辐射估计,得出最终光强。     

重要度驱动的自适应Projection Map在光子图中的应用

在全局光照算法中,光子图算法是一种与视点无关的物空间辐射度近似计算方法,提出了一种基于重要度驱动采样的自适应Projection map算法,利用它可以在光子图算法中提高光子发射的有效性和准确性,加快渲染速度并取得更好的图像质量.实验表明,该算法能够有效地减少光源发射光子的次数,提高光子的命中率,具有相当的应用价值.     

基于非线性最小二乘的图像自适应SR重建以及运动估计

图像超分辨率（SR）重建是利用数字信号处理技术由一系列低分辨率观测图像得到高分辨率图像。为了扩展SR技术的应用范围,提出了一种同时进行图像超分辨率重建和全局运动估计的方法。该方法首先基于最大后验概率(MAP)给出了图像SR重建和运动估计框架,该框架不仅考虑了前后两次迭代所得的HR图像差值对最终重建图像的影响,而且引入了不同LR图像对重建图像的重要性权值,使得算法具有自适应性;然后将总体框架转换为图像SR重建模型和运动估计模型;最后基于非线性最小二乘法对模型进行优化求解,得出了SR重建图像及其全局运动域。实验表明,该方法不仅图像重建效果良好,并有着良好的收敛性。     

基于物理模型的低照度图像增强算法

针对低照度图像反转后为与雾天图像相似的伪雾图,其雾的浓度由光照情况而非景深决定这一特点,提出一种基于物理模型的低照度图像增强算法。该算法根据光照情况给出一种更加准确且快速的新方法估计伪雾图的透射率。首先,采用暗原色先验规律对伪雾图的环境光值进行估计,并基于光照情况对透射率进行估计;然后,基于大气散射模型还原出无雾图像;最后,对无雾图像反转得到低照度图像的增强结果,并对该结果进行细节补偿得到最终的增强图像。大量实验表明,与基于暗原色先验的增强算法、基于去雾技术的增强算法及带色彩恢复的多尺度Retinex算法相比,该算法处理效率更高且效果良好,信息不会丢失,可有效提高图像分析识别等系统的工作效率。     

基于特征点提取的图像增强算法

特征提取被广泛的运用在图像处理和计算机视觉领域.而特征点提取的效果很大程度上取决于图像的质量.在室外,考虑到多变的光照条件图像的质量通常很差.除此以外,物体间的互相遮挡也是特征提取上的一个难题.因此,研究怎样实时提取低对比度图像是一个很具挑战性的问题.Retinex理论被认为是分离光照图和反射物体图从而得到补充光照图的良好办法.但是亮度图像重建的计算复杂度很高.本文提出了一种基于Retinex理论的图像增强算法.新的算法优化了可变框架的Retinex理论.文中提出一种新的卷积函数,其能有效的减少计算时间,并将多尺度和卷积函数结合起来.文章的最后提出了一种能衡量提取特征点有效性的评估实验.实验结果证明,新的算法不仅能够极大的减少处理时间从而满足实时性要求,而且能使增强后的图像更适合与特征点提取.     

基于生成对抗模型及光路分解的全局光照绘制

针对现有全局光照图像重建高频特征效果模糊的问题,提出一种基于生成对抗模型及光路分解的全局光照绘制网络,以各类图形辅助属性（法线、深度、粗糙度等）为主要输入,学习光照传输的抽象表示并编码,用于推理光照图像。第一,将光照解耦为漫反射和镜面反射两部分,设计独立的生成对抗网络端到端地学习和推理光照子图,避免混频光照的相互干扰,保证高频细节的清晰重现。第二,使用自编码器作为绘制网络的基本结构,添加多尺度特征融合模块用于不同感受野下的特征合成,以促进阴影、镜面反射等复杂特效的有效表达。第三,使用旋转损失和特征损失两种增强的对抗损失函数,增加网络训练的稳定性。实验结果表明,与现有降噪或图像生成模型相比,该方法能够有效地生成视觉上更逼真的全局光照图像,保留更多高频细节,PSNR指标提升8%～20%。     

基于光子映射的辐照度缓存算法

提出了一种全局光照计算方法,结合了两个知名的技术,光子映射和辐照度缓存.光子映射具有视点无关的优势,辐照度缓存可以快速计算间接光照,但后者是视点相关的,为了使光照缓存记录覆盖整个场景,辐照度缓存算法需要手动设置很多相机.利用这两种技术的各自优势,通过光子图来计算改进后的视点无关的辐照度缓存算法,实现了快速而准确的全局光...     

基于图像分解的光照估计算法

增强现实技术的目的在于将计算机生成的虚拟物体叠加到真实场景中。实现良好的虚实融合需要对场景光照进行估算,针对高光场景,利用场景中的不同反射光信息对场景进行有效的光照估计,首先通过基于像素聚类方法的图像分解对图像进行反射光的分解,得到漫反射图和镜面反射图,对漫反射图进行进一步的本征图像分解,得到反照率图和阴影图;之后结合分解结果和场景深度对输入图像的光照信息进行计算;最后使用全局光照模型对虚拟物体进行渲染,可以得到虚实场景高度融合的光照效果。     

基于信息熵的蒙特卡罗全局光照的自适应抽样

在真实感图形生成领域里，蒙特卡罗方法是计算整体光照问题的极佳选择。但是，在用基于蒙特卡罗的全局光照算法生成的图像中，当没有足够多的采样量的时候，存在大量的噪声。自适应抽样方法是减少这种噪声的一种很好的方法。该文提出了一种新的基于信息熵的自适应抽样算法。实验结果表明，该方法的效果优于香农信息熵等经典方法。     

光子映射在CUDA中的研究与实现

通过修改光子映射算法的实现过程,使得该算法能够通过CUDA完全运行在最新的GPU上,从而能够充分利用GPU强大的并行计算能力,加速光子映射的实现。光子映射在CUDA中的实现主要通过两个方面来完成：构建光子图和估计辐射能。同时为了提高对光子图中的光子信息的查找速度,采用了kd-tree结构来存储光子信息,使得可以通过KNN（K-Nearest Neighbor）快速搜索光子图。在所测试环境中,渲染速度是CPU中的近1O倍。     

Photon Differential Splatting for Rendering Caustics

We present a photon splatting technique which reduces noise and blur in the rendering of caustics. Blurring of illumination edges is an inherent problem in photon splatting, as each photon is unaware of its neighbours when being splatted. This means that the splat size is usually based on heuristics rather than knowledge of the local flux density. We use photon differentials to determine the size and shape of the splats such that we achieve adaptive anisotropic flux density estimation in photon splatting. As compared to previous work that uses photon differentials, we present the first method where no photons or beams or differentials need to be stored in a map. We also present improvements in the theory of photon differentials, which give more accurate results and a faster implementation. Our technique has good potential for GPU acceleration, and we limit the number of parameters requiring user adjustment to an overall smoothing parameter and the number of photons to be traced.     

Adaptive Caustic Maps Using Deferred Shading

Caustic maps provide an interactive image-space method to render caustics, the focusing of light via reflection and refraction. Unfortunately, caustic mapping suffers problems similar to shadow mapping: aliasing from poor sampling and map projection as well as temporal incoherency from frame-to-frame sampling variations. To reduce these problems, researchers have suggested methods ranging from caustic blurring to building a multiresolution caustic map. Yet these all require a fixed photon sampling, precluding the use of importance-based photon densities. This paper introduces adaptive caustic maps. Instead of densely sampling photons via a rasterization pass, we adaptively emit photons using a deferred shading pass. We describe deferred rendering for refractive surfaces, which speeds rendering of refractive geometry up to 25% and with adaptive sampling speeds caustic rendering up to 200%. These benefits are particularly noticable for complex geometry or using millions of photons. While developed for a GPU rasterizer, adaptive caustic map creation can be performed by any renderer that individually traces photons, e.g., a GPU ray tracer.     

Deep Kernel Density Estimation for Photon Mapping

Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high-quality reconstructions. In this paper, we develop the first deep learning-based method for particle-based rendering, and specifically focus on photon density estimation, the core of all particle-based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per-photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per-photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods. Our approach largely reduces the required number of photons, significantly advancing the computational efficiency in photon mapping.     

Improved Stochastic Progressive Photon Mapping with Metropolis Sampling

This paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis‐Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well‐designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.     

Caustic Forecasting: Unbiased Estimation of Caustic Lighting for Global Illumination

We present an unbiased method for generating caustic lighting using importance sampled Path Tracing with Caustic Forecasting. Our technique is part of a straightforward rendering scheme which extends the Illumination by Weak Singularities method to allow for fully unbiased global illumination with rapid convergence. A photon shooting preprocess, similar to that used in Photon Mapping, generates photons that interact with specular geometry. These photons are then clustered, effectively dividing the scene into regions which will contribute similar amounts of caustic lighting to the image. Finally, the photons are stored into spatial data structures associated with each cluster, and the clusters themselves are organized into a spatial data structure for fast searching. During rendering we use clusters to decide the caustic energy importance of a region, and use the local photons to aid in importance sampling, effectively reducing the number of samples required to capture caustic lighting.     

Multi‐Image Based Photon Tracing for Interactive Global Illumination of Dynamic Scenes

Image space photon mapping has the advantage of simple implementation on GPU without pre‐computation of complex acceleration structures. However, existing approaches use only a single image for tracing caustic photons, so they are limited to computing only a part of the global illumination effects for very simple scenes. In this paper we fully extend the image space approach by using multiple environment maps for photon mapping computation to achieve interactive global illumination of dynamic complex scenes. The two key problems due to the introduction of multiple images are 1) selecting the images to ensure adequate scene coverage; and 2) reliably computing ray‐geometry intersections with multiple images. We present effective solutions to these problems and show that, with multiple environment maps, the image‐space photon mapping approach can achieve interactive global illumination of dynamic complex scenes. The advantages of the method are demonstrated by comparison with other existing interactive global illumination methods.