Progressive Photon Mapping Research Articles

Hypothesis Testing for Progressive Kernel Estimation and VCM Framework.

Identifying an appropriate radius for unbiased kernel estimation is crucial for the efficiency of radiance estimation. However, determining both the radius and unbiasedness still faces big challenges. In this paper, we first propose a statistical model of photon samples and associated contributions for progressive kernel estimation, under which the kernel estimation is unbiased if the null hypothesis of this statistical model stands. Then, we present a method to decide whether to reject the null hypothesis about the statistical population (i.e., photon samples) by the F-test in the Analysis of Variance. Hereby, we implement a progressive photon mapping (PPM) algorithm, wherein the kernel radius is determined by this hypothesis test for unbiased radiance estimation. Second, we propose VCM+, a reinforcement of Vertex Connection and Merging (VCM), and derive its theoretically unbiased formulation. VCM+ combines hypothesis testing-based PPM with bidirectional path tracing (BDPT) via multiple importance sampling (MIS), wherein our kernel radius can leverage the contributions from PPM and BDPT. We test our new algorithms, improved PPM and VCM+, on diverse scenarios with different lighting settings. The experimental results demonstrate that our method can alleviate light leaks and visual blur artifacts of prior radiance estimate algorithms. We also evaluate the asymptotic performance of our approach and observe an overall improvement over the baseline in all testing scenarios.

Progressive photon mapping algorithm for digital imaging of a space target.

Space target imaging simulation holds great significance for the development of space-based imaging systems and the prediction of imaging tasks. Currently, commonly used rendering algorithms in space target imaging simulation suffer from issues such as radiance calculation results lacking actual physical significance and rendered images containing high-frequency noise. To address these issues, this study proposes a rendering algorithm based on progressive photon mapping in the context of space imaging scenarios, aiming to enhance the accuracy of energy calculations and image rendering for space targets. This algorithm generates multiple photon maps on the target surface through multiple iterations, retrieves photon information near the observation point based on these photon maps, and thus obtains the radiance of the observation direction vector. This study evaluates the quality of rendered images using no-reference image quality assessment algorithms. The results demonstrate that this algorithm can enhance image rendering quality in specific imaging scenarios, consequently improving the accuracy of space target recognition. By comparing the calculated values of this algorithm with the theoretical radiance values for diffuse material, the accuracy of the radiance calculation results of this algorithm is verified, which can provide significant reference values for the selection of backend detectors.

Consistent Post‐Reconstruction for Progressive Photon Mapping

AbstractPhoton mapping is a light transport algorithm that simulates various rendering effects (e.g., caustics) robustly, and its progressive variants, progressive photon mapping (PPM) methods, can produce a biased but consistent rendering output. PPM estimates radiance using a kernel density estimation whose parameters (bandwidths) are adjusted progressively, and this refinement enables to reduce its estimation bias. Nonetheless, many iterations (and thus a large number of photons) are often required until PPM produces nearly converged estimates. This paper proposes a post‐reconstruction that improves the performance of PPM by reducing residual errors in PPM estimates. Our key idea is to take multiple PPM estimates with multi‐level correlation structures, and fuse the input images using a weight function trained by supervised learning with maintaining the consistency of PPM. We demonstrate that our technique boosts an existing PPM technique for various rendering scenes.

Novel accelerated Stochastic Progressive Photon Mapping rendering with neural network

Recently, deep learning-based approaches have led to dramatic improvements for Monte Carlo rendering at the low sampling rate. Most of these approaches are aimed at path tracing. However, they are not suitable for photon mapping. In this paper, we develop a novel accelerate stochastic progressive photon mapping approaches with neural network. First, our framework utilizes the particle-based rendering and focuses on photon density estimation. We train a neural network to predict a kernel function to aggregate photon contributions at shading point. Then we construct a estimation images with the prediction network. During experiments, we could find that there are spike pixels and noises in estimation images sometimes. So we present the improved denoising network to post-process the estimation images. Finally, we can obtain the high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared with previous photon mapping methods. Besides, our denoising network can reduce most multi-scale noises on both low-frequency and high-frequency areas while preserving more illumination details, especially caustics, compared with other state-of-the-art learning-based denoising methods.

CPPM

We present a novel chi-squared progressive photon mapping algorithm (CPPM) that constructs an estimator by controlling the bandwidth to obtain superior image quality. Our estimator has parametric statistical advantages over prior nonparametric methods. First, we show that when a probability density function of the photon distribution is subject to uniform distribution, the radiance estimation is unbiased under certain assumptions. Next, the local photon distribution is evaluated via a chi-squared test to determine whether the photons follow the hypothesized distribution (uniform distribution) or not. If the statistical test deems that the photons inside the bandwidth are uniformly distributed, bandwidth reduction should be suspended. Finally, we present a pipeline with a bandwidth retention and conditional reduction scheme according to the test results. This pipeline not only accumulates sufficient photons for a reliable chi-squared test, but also guarantees that the estimate converges to the correct solution under our assumptions. We evaluate our method on various benchmarks and observe significant improvement in the running time and rendering quality in terms of mean squared error over prior progressive photon mapping methods.

Rasterisation-based progressive photon mapping

Ray tracing on the GPU has been synergistically operating alongside rasterisation in interactive rendering engines for some time now, in order to accurately capture certain illumination effects. In the same spirit, in this paper, we propose an implementation of progressive photon mapping entirely on the rasterisation pipeline, which is agnostic to the specific GPU architecture, in order to synthesise images at interactive rates. While any GPU ray tracing architecture can be used for photon mapping, performing ray traversal in image space minimises acceleration data structure construction time and supports arbitrarily complex and fully dynamic geometry. Furthermore, this strategy maximises data structure reuse by encompassing rasterisation, ray tracing and photon gathering tasks in a single data structure. Both eye and light paths of arbitrary depth are traced on multi-view deep G-buffers, and photon flux is gathered by a properly adapted multi-view photon splatting. In contrast to previous methods exploiting rasterisation to some extent, due to our novel indirect photon splatting approach, any event combination present in photon mapping is captured. We evaluate our method using typical test scenes and scenarios for photon mapping methods and show how our approach outperforms typical GPU-based progressive photon mapping.

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.

Multiple Photon Sampling Technique Based on Stochastic Progressive Photon Mapping

This paper introduces a multiple photon sampling technique based on stochastic progressive photon mapping. We use the image space concept to divide the scene into continuous sub-blocks and then we calculate our proposed distance function and photon number function in each of the sub-blocks. The distance function is used to calculate the distance error of the hit point and to determine whether each sub-block is located at a boundary between different objects. The photon number function is used to calculate the photon number error and to determine whether the photon distribution in each sub-block is uniform. Based on the values of the distance error and the photon number error, the multiple photon sampling technique is used to acquire multiple samples of the hit point in each sub-block. Instead of using a single radius for the radiance estimate, we use three different radii and compute the final radiance estimate as a weighted average of the three values. When compared with the existing stochastic progressive photon mapping method, our method provides a better solution to the photon distribution problem and can also reduce bias and noise, especially in the scene with drastic changes in light and dark.

Progressive Photon Elimination With a Status Tree

We describe a modified progressive photon mapping (PPM) method applied with sample elimination, referred to as progressive photon elimination, to pursue accurate results and accelerated iterations. Meanwhile, an elimination status tree is proposed for progressive photon elimination, which retains the information used in the elimination and can be updated along with the multi-pass process to solve the challenge of the accuracy of local parameters. By using the status tree, our method can obtain a uniform photon distribution at each iteration by eliminating a certain number of photons. The tree is also improved to adapt to the complicated photon distributions. This strategy also facilitates the parallel elimination with a priori elimination ratio in the status tree, making the algorithm further accelerated. In parallel block processing, the equilibrium and edge problems are resolved. The experimental results show that our method requires about half of the number of iterations to achieve the same visual effect compared with progressive photon mapping.

Efficient Progressive Radiance Estimation Engine Architecture and Implementation for Progressive Photon Mapping

We propose a progressive radiance estimation engine (PREE) hardware architecture to accelerate the processing of the progressive photon mapping with satisfactory graphic quality. The presented PREE architecture consists of four progressive radiance estimation units (PREUs), approximate full task schedule-oriented hit-point update operation controller (AFTSO-HpUOC) and approximate data-independent schedule-oriented radiance evaluation controller (ADISO-REC). The PREUs accelerate the radiance estimation computation by a pipeline technique and share and configure the hardware resource for hit-point update operation and radiance evaluation. Through AFTSO-HpUOC and ADISO-REC, the data can be efficiently dispatched to achieve better parallelism and the data dependence can be alleviated within the four PREUs, respectively. The core area of the proposed PREE architecture implemented in TSMC 90-nm CMOS process is 1.78 mm2. According to the post-layout simulation results, the implementation achieves 496.79 million hit-point update operations per second (MHpUO/s) and consumes 184 mW at 125 MHz for Cornell box with three balls.

Time‐varying light motion in single convergence

AbstractAs light travels fast in the physical world, it is generally hard to capture the propagation of light with a real camera. However, with the development of photon mapping, it is possible to simulate and visualize such fantastic effect. In this paper, we propose a new algorithm based on progressive photon mapping to render the time‐varying light motion. By incorporating our algorithm with participating media, we synthesis the animation of time‐varying light beams expanding in space with slow motion. Interesting phenomena can be observed in our experiments through designating either a constant light source or a bullet light that only emits instantaneously. Our algorithm is efficient in the sense that all frames of the animation can be rendered with only one convergence of the progressive photon mapping, showing the advantage of revealing the light propagation process by our algorithm. The experiments demonstrate the effectiveness and efficiency of our approach.

Photon mapping with visible kernel domains

Despite the strong efforts made in the last three decades, lighting simulation systems still remain prone to various types of imprecisions. This paper specifically tackles the problem of biases due to density estimation used in photon mapping approaches. We study the fundamental aspects of density estimation and exhibit the need for handling visibility in the early stage of the kernel domain definition. We show that properly managing visibility in the density estimation process allows to reduce or to remove biases all at once. In practice, we have implemented a 3D product kernel based on a polyhedral domain, with both point-to-point and point-to-surface visibility computation. Our experimental results illustrate the enhancements produced at every stage of density estimation, for direct photon maps visualization and progressive photon mapping.

A Spatial Target Function for Metropolis Photon Tracing

The human visual system is sensitive to relative differences in luminance, but light transport simulation algorithms based on Metropolis sampling often result in a highly nonuniform relative error distribution over the rendered image. Although this issue has previously been addressed in the context of the Metropolis light transport algorithm, our work focuses on Metropolis photon tracing. We present a new target function (TF) for Metropolis photon tracing that ensures good stratification of photons leading to pixel estimates with equalized relative error. We develop a hierarchical scheme for progressive construction of the TF from paths sampled during rendering. In addition to the approach taken in previous work, where the TF is defined in the image plane, ours can be associated with compact spatial regions. This allows us to take advantage of illumination coherence to more robustly estimate the TF while adapting to geometry discontinuities. To sample from this TF, we design a new replica exchange Metropolis scheme. We apply our algorithm in progressive photon mapping and show that it often outperforms alternative approaches in terms of image quality by a large margin.

A spatial target function for metropolis photon tracing

The human visual system is sensitive to relative differences in luminance, but light transport simulation algorithms based on Metropolis sampling often result in a highly nonuniform relative error distribution over the rendered image. Although this issue has previously been addressed in the context of the Metropolis light transport algorithm, our work focuses on Metropolis photon tracing. We present a new target function (TF) for Metropolis photon tracing that ensures good stratification of photons leading to pixel estimates with equalized relative error. We develop a hierarchical scheme for progressive construction of the TF from paths sampled during rendering. In addition to the approach taken in previous work, where the TF is defined in the image plane, ours can be associated with compact spatial regions. This allows us to take advantage of illumination coherence to more robustly estimate the TF while adapting to geometry discontinuities. To sample from this TF, we design a new replica exchange Metropolis scheme. We apply our algorithm in progressive photon mapping and show that it often outperforms alternative approaches in terms of image quality by a large margin.

A Spatial Target Function for Metropolis Photon Tracing

The human visual system is sensitive to relative differences in luminance, but light transport simulation algorithms based on Metropolis sampling often result in a highly nonuniform relative error distribution over the rendered image. Although this issue has previously been addressed in the context of the Metropolis light transport algorithm, our work focuses on Metropolis photon tracing. We present a new target function (TF) for Metropolis photon tracing that ensures good stratification of photons leading to pixel estimates with equalized relative error. We develop a hierarchical scheme for progressive construction of the TF from paths sampled during rendering. In addition to the approach taken in previous work, where the TF is defined in the image plane, ours can be associated with compact spatial regions. This allows us to take advantage of illumination coherence to more robustly estimate the TF while adapting to geometry discontinuities. To sample from this TF, we design a new replica exchange Metropolis scheme. We apply our algorithm in progressive photon mapping and show that it often outperforms alternative approaches in terms of image quality by a large margin.

Stylized Caustics: Progressive Rendering of Animated Caustics

AbstractIn recent years, much work was devoted to the design of light editing methods such as relighting and light path editing. So far, little work addressed the target‐based manipulation and animation of caustics, for instance to a differently‐shaped caustic, text or an image. The aim of this work is the animation of caustics by blending towards a given target irradiance distribution. This enables an artist to coherently change appearance and style of caustics, e.g., for marketing applications and visual effects. Generating a smooth animation is nontrivial, as photon density and caustic structure may change significantly. Our method is based on the efficient solution of a discrete assignment problem that incorporates constraints appropriate to make intermediate blends plausibly resemble caustics. The algorithm generates temporally coherent results that are rendered with stochastic progressive photon mapping. We demonstrate our system in a number of scenes and show blends as well as a key frame animation.

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.

RayPortals: a light transport editing framework

Physically based rendering, using path-space formulation of global illumination, has become a standard technique for high-quality computer-generated imagery. Nonetheless, being able to control and edit the resulting picture so that it corresponds to the artist vision is still a tedious trial-and-error process. We show how the manipulation of light transport translates into the path-space integral formulation of the rendering equation. We introduce portals as a path-space manipulation tool to edit and control renderings and show how our editing tool unifies and extends previous work on lighting editing. Portals allow the artist to precisely control the final aspect of the image without modifying neither scene geometry nor lighting setup. According to the setup of two geometric handles and a simple path selection filter, portals capture specific lightpaths and teleport them through 3D space. We implement portals in major path-based algorithms (Photon Mapping, Progressive Photon Mapping and Bi-directional Path Tracing) and demonstrate the wide range of control this technique allows on various lighting effects, from low-frequency color bleeding to high-frequency caustics as well as view-dependent reflections.

Consistent Scene Editing by Progressive Difference Images

AbstractEven though much research was dedicated to the acceleration of consistent, progressive light transport simulations, the computation of fully converged images is still very time‐consuming. This is problematic, as for the practical use in production pipelines, the rapid editing of lighting effects is important. While previous approaches restart the simulation with every scene manipulation, we make use of the coherence between frames before and after a modification in order to accelerate convergence of the context that remained similar. This is especially beneficial if a scene is edited that has already been converging for a long time, because much of the previous result can be reused, e.g., sharp caustics cast or received by the unedited scene parts. In its essence, our method performs the scene modification stochastically by predicting and accounting for the difference image. In addition, we employ two heuristics to handle cases in which stochastic removal is likely to lead to strong noise. Typical scene interactions can be broken down into object adding and removal, material substitution, camera movement and light editing, which we all examine in a number of test scenes both qualitatively and quantitatively. As we focus on caustics, we chose stochastic progressive photon mapping as the underlying light transport algorithm. Further, we show preliminary results of bidirectional path tracing and vertex connection and merging.

Progressive photon mapping for daylight redirecting components

Daylight redirecting components (DRCs) are characterised by complex transmissive and reflective behaviour that is difficult to predict accurately largely due to their highly directional scattering, and the caustics this produces. This paper examines the application of progressive photon mapping as a state of the art forward raytracing technique to efficiently simulate the behaviour of such DRCs, and how this approach can support architects in assessing their performance.Progressive photon mapping is an iterative variant of static photon mapping that effects noise reduction through accumulation of results, as well as a reduction in bias inherent to all density estimation methods by reducing the associated bandwidth at a predetermined rate. This not only results in simplified parametrisation for the user, but also provides a preview of the progressively refined simulation, thus making the tool accessible to non-experts as well.We demonstrate the effectiveness of this technique with an implementation based on the Radiancephoton mapping extension and a case study involving retroreflecting prismatic blinds as a representative DRC.