Global Illumination using Photon Ray Splatting

We present a novel framework for efficiently computing the indirect illumination in diffuse and moderately glossy scenes using density estimation techniques. Many existing global illumination approaches either quickly compute an overly approximate solution or perform an orders of magnitude slower computation to obtain high-quality results for the indirect illumination. The proposed method improves photon density estimation and leads to significantly better visual quality in particular for complex geometry, while only slightly increasing the computation time. We perform direct splatting of photon rays, which allows us to use simpler search data structures. Since our density estimation is carried out in ray space rather than on surfaces, as in the commonly used photon mapping algorithm, the results are more robust against geometrically incurred sources of bias. This holds also in combination with final gathering where photon mapping often overestimates the illumination near concave geometric features. In addition, we show that our photon splatting technique can be extended to handle moderately glossy surfaces and can be combined with traditional irradiance caching for sparse sampling and filtering in image space.     

Progressive Expectation‐Maximization for Hierarchical Volumetric Photon Mapping

State‐of‐the‐art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel‐based approaches is the excessive number of photons that are required in practice to resolve fine illumination details, while controlling the amount of noise. In this paper, we propose a parametric density estimation technique that represents radiance using a hierarchical Gaussian mixture. We efficiently obtain the coefficients of this mixture using a progressive and accelerated form of the Expectation‐Maximization algorithm. After this step, we are able to create noise‐free renderings of high‐frequency illumination using only a few thousand Gaussian terms, where millions of photons are traditionally required. Temporal coherence is trivially supported within this framework, and the compact footprint is also useful in the context of real‐time visualization. We demonstrate a hierarchical ray tracing‐based implementation, as well as a fast splatting approach that can interactively render animated volume caustics.     

基于栅格的全局光子图重建

基于光子图的光子映射算法能产生高质量的照片级图像。对于光照复杂的 场景,光子图需要存储大量光子以提高生成图像的质量,这不仅占用大量的内存空间,而且 光照估计的时间长。论文提出基于栅格的全局光子图重建的算法,即在光子包围盒被栅格化 后,其非空栅格中一定比例的光子被用来重建新的光子图,并保证重建前后栅格内光子能量 和守恒,这使得重建前后光子图的光照估计的效果相近。通过增加特定栅格中的重建光子数 目,能有效减少由几何偏差引起的光照估计误差,增强直接聚焦（焦散）和间接聚焦光照的 绘制效果;并使用简单方法检测生成图像中少量噪声,增加少量采样即可有效减少相应的噪 声。全局光子图重建算法的计算成本低,并保持生成图像的视觉独立性。     

The Beam Radiance Estimate for Volumetric Photon Mapping

We present a new method for efficiently simulating the scattering of light within participating media. Using a theoretical reformulation of volumetric photon mapping, we develop a novel photon gathering technique for participating media. Traditional volumetric photon mapping samples the in‐scattered radiance at numerous points along the length of a single ray by performing costly range queries within the photon map. Our technique replaces these multiple point‐queries with a single beam‐query, which explicitly gathers all photons along the length of an entire ray. These photons are used to estimate the accumulated in‐scattered radiance arriving from a particular direction and need to be gathered only once per ray. Our method handles both fixed and adaptive kernels, is faster than regular volumetric photon mapping, and produces images with less noise.     

Spectral Ray Differentials

Light refracted by a dispersive interface leads to beautifully colored patterns that can be rendered faithfully with spectral Monte‐Carlo methods. Regrettably, results often suffer from chromatic noise or banding, requiring high sampling rates and large amounts of memory compared to renderers operating in some trichromatic color space. Addressing this issue, we introduce spectral ray differentials, which describe the change of light direction with respect to changes in the spectrum. In analogy with the classic ray and photon differentials, this information can be used for filtering in the spectral domain. Effectiveness of our approach is demonstrated by filtering for offline spectral light and path tracing as well as for an interactive GPU photon mapper based on splatting. Our results show considerably less chromatic noise and spatial aliasing while retaining good visual similarity to reference solutions with negligible overhead in the order of milliseconds.     

Interactive Global Photon Mapping

We present a photon mapping technique capable of computing high quality global illumination at interactive frame rates. By extending the concept of photon differentials to efficiently handle diffuse reflections, we generate footprints at all photon hit points. These enable illumination reconstruction by density estimation with variable kernel bandwidths without having to locate the k nearest photon hits first. Adapting an efficient BVH construction process for ray tracing acceleration, we build photon maps that enable the fast retrieval of all hits relevant to a shading point. We present a heuristic that automatically tunes the BVH build's termination criterion to the scene and illumination conditions. As all stages of the algorithm are highly parallelizable, we demonstrate an implementation using NVidia's CUDA manycore architecture running at interactive rates on a single GPU. Both light source and camera may be freely moved with global illumination fully recalculated in each frame.     

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.     

Splatting errors and antialiasing

The paper describes three new results for volume rendering algorithms utilizing splatting. First, an antialiasing extension to the basic splatting algorithm is introduced that mitigates the spatial aliasing for high resolution volumes. Aliasing can be severe for high resolution volumes or volumes where a high depth of field leads to converging samples along the perspective axis. Next, an analysis of the common approximation errors in the splatting process for perspective viewing is presented. In this context, we give different implementations, distinguished by efficiency and accuracy, for adding the splat contributions to the image plane. We then present new results in controlling the splatting errors and also show their behavior in the framework of our new antialiasing technique. Finally, current work in progress on extensions to splatting for temporal antialiasing is demonstrated. We present a simple but highly effective scheme for adding motion blur to fast moving volumes     

Hierarchical Photon Mapping

Photon mapping is an efficient method for producing high-quality, photorealistic images with full global illumination. In this paper we present a more accurate and efficient approach to final gathering using the photon map based upon hierarchical evaluation of the photons over each surface. We use the footprint of each gather ray to calculate the irradiance estimate area rather than deriving it from the local photon density. We then describe an efficient method for computing the irradiance from the photon map given an arbitrary estimate area. Finally, we demonstrate how the technique may be used to reduce variance and increase efficiency when sampling diffuse and glossy-specular BRDFs.     

Grouped photon mapping

This paper proposes a novel architecture called Grouped Photon Mapping, which combines standard photon mapping with the light-beam concept to improve the nearest-neighbor density estimation method. Based on spatial coherence, we cluster all of photons, which are deposited in the photon map, into different beam-like groups. Each group of photons is individually stored in an isolated photon map. By the distribution of the photons in each photon map, we construct a polygonal boundary to represent a beam-like illuminated area. These boundaries offer a more accurate and flexible sampling area to filter neighbor photons around the query point. In addition, by a level of detail technique, we can control the photon-count in each group to obtain a balance between biases and noise. The results of our experiments prove that our method can successfully reduce bias errors and light leakage. Especially, for complicated caustic effects through a gemstone-like object, we can render a smoother result than standard photon mapping.     

Efficient Caustic Rendering with Lightweight Photon Mapping

Robust and efficient rendering of complex lighting effects, such as caustics, remains a challenging task. While algorithms like vertex connection and merging can render such effects robustly, their significant overhead over a simple path tracer is not always justified and – as we show in this paper ‐ also not necessary. In current rendering solutions, caustics often require the user to enable a specialized algorithm, usually a photon mapper, and hand‐tune its parameters. But even with carefully chosen parameters, photon mapping may still trace many photons that the path tracer could sample well enough, or, even worse, that are not visible at all. Our goal is robust, yet lightweight, caustics rendering. To that end, we propose a technique to identify and focus computation on the photon paths that offer significant variance reduction over samples from a path tracer. We apply this technique in a rendering solution combining path tracing and photon mapping. The photon emission is automatically guided towards regions where the photons are useful, i.e., provide substantial variance reduction for the currently rendered image. Our method achieves better photon densities with fewer light paths (and thus photons) than emission guiding approaches based on visual importance. In addition, we automatically determine an appropriate number of photons for a given scene, and the algorithm gracefully degenerates to pure path tracing for scenes that do not benefit from photon mapping.     

Distributed Out‐of‐Core Stochastic Progressive Photon Mapping

At present, stochastic progressive photon mapping (SPPM) is one of the most comprehensive methods for a consistent global illumination computation. Even though the number of photons is unlimited due to their progressive nature, the scene size is still bound by the available main memory. In this paper, we present the first consistent out‐of‐core SPPM algorithm. In order to cope with large scenes, we automatically subdivide the geometry and parallelly trace photons and eye rays in a portal‐based system, distributed across multiple machines in a commodity cluster. Moreover, modifications of the original SPPM method are introduced that keep both the utilization of tracer machines high and the network traffic low. Therefore, compared to a portal‐based single machine setup, our distributed approach achieves a significant speedup. We compare a GPU‐based with a CPU‐based implementation and demonstrate our system in multiple large test scenes of up to 90 million triangles.     

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.     

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.     

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。     

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。     

A parallel Monte Carlo transport algorithm using a pseudo-random tree to guarantee reproducibility

We present a parallel Monte Carlo photon transport algorithm that insures the reproducibility of results. The important feature of this parallel implementation is the introduction of a pair of pseudo-random number generators. This pair of generators is structured in such a manner as to insure minimal correlation between the two sequences of pseudo-random numbers produced. We term this structure as a ‘pseudo-random tree’. Using this structure, we are able to reproduce results exactly in a asynchronous parallel processing environment. The algorithm tracks the history of photons as they interact with two carbon cylinders joined end to end. The algorithm was implemented on both a Denelcor HEP and a CRAY X-MP/48. We describe the algorithm and the pseudo-random tree structure and present speedup results of our implementation.     

基于分集接收的单光子探测技术

单光子三维探测技术因为在微弱信号目标的光学探测方面的独特优势，在空间目标探测、空中目标侦查、远距离预警探测等方面有重要应用，近年来获得了广泛关注。对主动扫描成像的单光子探测系统，为扩展作用距离和提高扫描速度，接收口径和接收视场角不断增大。但对高集成的盖革APD型的单光子探测器，大接收口径和大接收视场带来背景噪声光子数量的增大，这会导致探测器频繁进入死区，给扫描图像带来大量盲点。针对此问题，提出和通过仿真证明了，利用多镜头分集接收和联合单光子信息处理，可实现对单光子探测器死区时间抑制，进而有效提高系统探测概率，降低扫描图像的盲点数量，并通过试验系统进行了验证。     

基于超微弱光子辐射方法的生物死亡过程的自动化识别系统

提出了一种基于超微弱光子辐射（UPE）测量的生物体死亡过程识别系统。实验中使用实验医用老鼠来研究了老鼠死亡过程中发射的超微弱光子和其死亡过程的关系。通过比较老鼠在正常生存状态和死亡过程中辐射的超微弱光子发现,当老鼠在死亡之前其发射的超微弱光子数量会有一个明显的增加过程。本文的实验研究结果表明,超微弱光子辐射可以作为一种观察生物体生理活动和生存状态的良好方法。     

Interactive cloud rendering using temporally coherent photon mapping

This work presents a novel interactive algorithm for simulation of light transport in clouds. Exploiting the high temporal coherence of the typical illumination and morphology of clouds we build on volumetric photon mapping, which we modify to allow for interactive rendering speeds—instead of building a fresh irregular photon map for every scene state change we accumulate photon contributions in a regular grid structure. This is then continuously being refreshed by re-shooting only a fraction of the total amount of photons in each frame. To maintain its temporal coherence and low variance, a low-resolution grid is initially used, and is then upsampled to the density field resolution on a physical basis in each frame. We also present a technique to store and reconstruct the angular illumination information by exploiting properties of the standard Henyey–Greenstein function, namely its ability to express anisotropic angular distributions with a single dominating direction. The presented method is physically plausible, conceptually simple and comparatively easy to implement. Moreover, it operates only above the cloud density field, thus not requiring any precomputation, and handles all light sources typical for the given environment, i.e., where one of the light sources dominates.