Data distributed, parallel algorithm for ray-traced volume rendering

Journal ArticleThis paper presents a divide-and-conquer ray-traced volume rendering algorithm and a parallel image compositing method, along with their implementation and performance on the connection Machine CM-5, and networked workstations. This algorithm distributes both the data and the computations to individual processing units to achieve fast, high-quality rendering of high-resolution data. The volume data, once distributed, is left intact. The processing nodes perform local raytracing of their sub volume concurrently. No communication between processing units is needed during this locally ray-tracing process. A subimage is generated by each processing unit and the final image is obtained by compositing subimages in the proper order, which can be determined a priori. Test results on the CM-5 and a group of networked workstations demonstrate the practicality of our rendering algorithm and compositing method

A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy

In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the worst sunlight condition in a year. This requirement is a prerequisite for obtaining a building permit during the conceptual design of a residential project. Thus, officially sanctioned software is usually used to assess the sunlight performance of buildings. These software programs predict sunlight hours based on repeated shading calculations, which is time-consuming. This paper proposed a multilayer perceptron-based method, a one-stage prediction approach, which outputs a shading time interval caused by the inputted cuboid-form building. The sunlight hours of a site can be obtained by calculating the union of the sunlight time intervals (complement of shading time interval) of all the buildings. Three numerical experiments, i.e., horizontal level and slope analysis, and simulation-based optimization are carried out; the results show that the method reduces the computation time to 1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning plug-in for Rhino 7/Grasshopper is also developed based on the proposed model. This paper indicates that deep learning techniques can be adopted to accelerate sunlight hour simulations at the conceptual design phase

3D point cloud upsampling for accurate reconstruction of dense 2.5D thickness maps

This paper presents a novel robust processing methodology for computing 2.5D thickness maps from dense 3D collocated surfaces. The proposed pipeline is suitable to faithfully adjust data representation detailing as required, from preserving fine surface features to coarse interpretations. The foundations of the proposed technique exploit spatial point-based filtering, ray tracing techniques and the Robust Implicit Moving Least Squares (RIMLS) algorithm applied to dense 3D datasets, such as those acquired from laser scanners. The effectiveness of the proposed technique in overcoming traditional angular aliasing and corruption artifacts is validated with 3D ranging data acquired from internal and external surfaces of exhumed water pipes. It is shown that the resulting 2.5D maps can be more accurately and completely computed to higher resolutions, while significantly reducing the number of raytracing errors when compared with 2.5D thickness maps derived from our current approach

LightSpeed: Light and Fast Neural Light Fields on Mobile Devices

Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited computational power and storage. Using volumetric rendering methods, such as NeRF and its derivatives, on mobile devices is not suitable due to the high computational cost of volumetric rendering. On the other hand, recent advances in neural light field representations have shown promising real-time view synthesis results on mobile devices. Neural light field methods learn a direct mapping from a ray representation to the pixel color. The current choice of ray representation is either stratified ray sampling or Plucker coordinates, overlooking the classic light slab (two-plane) representation, the preferred representation to interpolate between light field views. In this work, we find that using the light slab representation is an efficient representation for learning a neural light field. More importantly, it is a lower-dimensional ray representation enabling us to learn the 4D ray space using feature grids which are significantly faster to train and render. Although mostly designed for frontal views, we show that the light-slab representation can be further extended to non-frontal scenes using a divide-and-conquer strategy. Our method offers superior rendering quality compared to previous light field methods and achieves a significantly improved trade-off between rendering quality and speed.Comment: Project Page: http://lightspeed-r2l.github.io/ . Add camera ready versio

Visibility-Based Optimizations for Image Synthesis

Katedra počítačové grafiky a interakce

Fortgeschrittene Entrauschungs-Verfahren und speicherlose Beschleunigungstechniken für realistische Bildsynthese

Stochastic ray tracing methods have become the industry's standard for today's realistic image synthesis thanks to their ability to achieve a supreme degree of realism by physically simulating various natural phenomena of light and cameras (e.g. global illumination, depth-of-field, or motion blur). Unfortunately, high computational cost for more complex scenes and image noise from insufficient simulations are major issues of these methods and, hence, acceleration and denoising are key components in stochastic ray tracing systems. In this thesis, we introduce two new filtering methods for advanced lighting and camera effects, as well as a novel approach for memoryless acceleration. In particular, we present an interactive filter for global illumination in the presence of depth-of-field, and a general and robust adaptive reconstruction framework for high-quality images with a wide range of rendering effects. To address complex scene geometry, we propose a novel concept which models the acceleration structure completely implicit, i.e. without any additional memory cost at all, while still allowing for interactive performance. Our contributions advance the state-of-the-art of denoising techniques for realistic image synthesis as well as the field of memoryless acceleration for ray tracing systems.Stochastische Ray-Tracing Methoden sind heutzutage der Industriestandard für realistische Bildsynthese, da sie einen hohen Grad an Realismus erzeugen können, indem sie verschiedene natürliche Phänomene (z.B. globale Beleuchtung, Tiefenunschärfe oder Bewegungsunschärfe) physikalisch korrekt simulieren. Offene Probleme dieser Verfahren sind hohe Rechenzeit für komplexere Szenen sowie Bildrauschen durch unzulängliche Simulationen. Demzufolge sind Beschleunigungstechniken und Entrauschungsverfahren essentielle Komponenten in stochastischen Ray-Tracing-Systemen. In dieser Arbeit stellen wir zwei neue Filter-Methoden für erweiterte Beleuchungs- und Kamera-Effekte sowie ein neuartiges Verfahren für eine speicherlose Beschleunigungsstruktur vor. Im Detail präsentieren wir einen interaktiven Filter für globale Beleuchtung in Kombination mit Tiefenunschärfe und einen generischen, robusten Ansatz für die adaptive Rekonstruktion von hoch-qualitativen Bildern mit einer großen Auswahl an Rendering-Effekten. Für das Problem hoher geometrischer Szenen-Komplexität demonstrieren wir ein neuartiges Konzept für die implizierte Modellierung der Beschleunigungsstruktur, welches keinen zusätzlichen Speicher verbraucht, aber weiterhin interaktive Laufzeiten ermöglicht. Unsere Beiträge verbessern sowohl den aktuellen Stand von Entrauschungs-Verfahren in der realistischen Bildsynthese als auch das Feld der speicherlosen Beschleunigungsstrukturen für Ray-Tracing-Systeme

The projector algorithm: a simple parallel algorithm for computing Voronoi diagrams and Delaunay graphs

The Voronoi diagram is a certain geometric data structure which has numerous applications in various scientific and technological fields. The theory of algorithms for computing 2D Euclidean Voronoi diagrams of point sites is rich and useful, with several different and important algorithms. However, this theory has been quite steady during the last few decades in the sense that no essentially new algorithms have entered the game. In addition, most of the known algorithms are serial in nature and hence cast inherent difficulties on the possibility to compute the diagram in parallel. In this paper we present the projector algorithm: a new and simple algorithm which enables the (combinatorial) computation of 2D Voronoi diagrams. The algorithm is significantly different from previous ones and some of the involved concepts in it are in the spirit of linear programming and optics. Parallel implementation is naturally supported since each Voronoi cell can be computed independently of the other cells. A new combinatorial structure for representing the cells (and any convex polytope) is described along the way and the computation of the induced Delaunay graph is obtained almost automatically.Comment: This is a major revision; re-organization and better presentation of some parts; correction of several inaccuracies; improvement of some proofs and figures; added references; modification of the title; the paper is long but more than half of it is composed of proofs and references: it is sufficient to look at pages 5, 7--11 in order to understand the algorith

Efficient representations of large radiosity matrices

The radiosity equation can be expressed as a linear system, where light interactions between patches of the scene are considered. Its resolution has been one of the main subjects in computer graphics, which has lead to the development of methods focused on different goals. For instance, in inverse lighting problems, it is convenient to solve the radiosity equation thousands of times for static geometries. Also, this calculation needs to consider many (or infinite) light bounces to achieve accurate global illumination results. Several methods have been developed to solve the linear system by finding approximations or other representations of the radiosity matrix, because the full storage of this matrix is memory demanding. Some examples are hierarchical radiosity, progressive refinement approaches, or wavelet radiosity. Even though these methods are memory efficient, they may become slow for many light bounces, due to their iterative nature. Recently, efficient methods have been developed for the direct resolution of the radiosity equation. In this case, the challenge is to reduce the memory requirements of the radiosity matrix, and its inverse. The main objective of this thesis is exploiting the properties of specific problems to reduce the memory requirements of the radiosity problem. Hereby, two types of problems are analyzed. The first problem is to solve radiosity for scenes with a high spatial coherence, such as it happens to some architectural models. The second involves scenes with a high occlusion factor between patches. For the high spatial coherence case, a novel and efficient error-bounded factorization method is presented. It is based on the use of multiple singular value decompositions along with a space filling curve, which allows to exploit spatial coherence. This technique accelerates the factorization of in-core matrices, and allows to work with out-of-core matrices passing only one time over them. In the experimental analysis, the presented method is applied to scenes up to 163K patches. After a precomputation stage, it is used to solve the radiosity equation for fixed geometries and infinite bounces, at interactive times. For the high occlusion problem, city models are used. In this case, the sparsity of the radiosity matrix is exploited. An approach for radiative exchange computation is proposed, where the inverse of the radiosity matrix is approximated. In this calculation, near-zero elements are removed, leading to a highly sparse result. This technique is applied to simulate daylight in urban environments composed by up to 140k patches.La ecuación de radiosidad tiene por objetivo el cálculo de la interacción de la luz con los elementos de la escena. Esta se puede expresar como un sistema lineal, cuya resolución ha derivado en el desarrollo de diversos métodos gráficos para satisfacer propósitos específicos. Por ejemplo, en problemas inversos de iluminación para geometrías estáticas, se debe resolver la ecuación de radiosidad miles de veces. Además, este cálculo debe considerar muchos (infinitos) rebotes de luz, si se quieren obtener resultados precisos de iluminación global. Entre los métodos desarrollados, se destacan aquellos que generan aproximaciones u otras representaciones de la matriz de radiosidad, debido a que su almacenamiento requiere grandes cantidades de memoria. Algunos ejemplos de estas técnicas son la radiosidad jerárquica, el refinamiento progresivo y la radiosidad basada en wavelets. Si bien estos métodos son eficientes en cuanto a memoria, pueden ser lentos cuando se requiere el cálculo de muchos rebotes de luz, debido a su naturaleza iterativa. Recientemente se han desarrollado métodos eficientes para la resolución directa de la ecuación de radiosidad, basados en el pre-cómputo de la inversa de la matriz de radiosidad. En estos casos, el desafío consiste en reducir los requerimientos de memoria y tiempo de ejecución para el cálculo de la matriz y de su inversa. El principal objetivo de la tesis consiste en explotar propiedades específicas de ciertos problemas de iluminación para reducir los requerimientos de memoria de la ecuación de radiosidad. En este contexto, se analizan dos casos diferentes. El primero consiste en hallar la radiosidad para escenas con alta coherencia espacial, tal como ocurre en algunos modelos arquitectónicos. El segundo involucra escenas con un elevado factor de oclusión entre parches. Para el caso de alta coherencia espacial, se presenta un nuevo método de factorización de matrices que es computacionalmente eficiente y que genera aproximaciones cuyo error es configurable. Está basado en el uso de múltiples descomposiciones en valores singulares (SVD) junto a una curva de recubrimiento espacial, lo que permite explotar la coherencia espacial. Esta técnica acelera la factorización de matrices que entran en memoria, y permite trabajar con matrices que no entran en memoria, recorriéndolas una única vez. En el análisis experimental, el método presentado es aplicado a escenas de hasta 163 mil parches. Luego de una etapa de precómputo, se logra resolver la ecuación de radiosidad en tiempos interactivos, para geométricas estáticas e infinitos rebotes. Para el problema de alta oclusión, se utilizan modelos de ciudades. En este caso, se aprovecha la baja densidad de la matriz de radiosidad, y se propone una técnica para el cálculo aproximado de su inversa. En este cálculo, los elementos cercanos a cero son eliminados. La técnica es aplicada a la simulación de la luz natural en ambientes urbanos compuestos por hasta 140 mil parches

Occlusion Modeling for Coherent Echo Data Simulation:A Comparison Between Ray-Tracing and Convex-Hull Methods

The ability to simulate realistic coherent datasets for synthetic aperture imaging systems is crucial for the design, development and evaluation of the sensors and their signal processing pipelines, machine learning algorithms and autonomy systems. In the case of synthetic aperture sonar (SAS), collecting experimental data is expensive and it is rarely possible to obtain ground truth of the sensor’s path, the speed of sound in the medium, and the geometry of the imaged scene. Simulating sonar echo data allows signal processing algorithms to be tested with known ground truth, enabling rapid and inexpensive development and evaluation of signal processing algorithms. The de-facto standard for simulating conventional high-frequency (i.e., > 100 kHz) SAS echo data from an arbitrary sensor, path and scene is to use a point-based or facet-based diffraction model. A crucial part of this process is acoustic occlusion modeling. This article describes a SAS simulation pipeline and compares implementations of two occlusion methods; ray-tracing, and a newer approximate method based on finding the convex hull of a transformed point cloud. The full capability of the simulation pipeline is demonstrated using an example scene based on a high-resolution 3D model of the SS Thistlegorm shipwreck which was obtained using photogrammetry. The 3D model spans a volume of 220 × 130 × 25 m and is comprised of over 30 million facets that are decomposed into a cloud of almost 1 billion points. The convex-hull occlusion model was found to result in simulated SAS imagery that is qualitatively indistinguishable from the ray-tracing approach and quantitatively very similar, demonstrating that use of this alternative method has potential to improve speed while retaining high fidelity of simulation.The convex-hull approach was found to be up to 4 times faster in a fair speed comparison with serial and parallel CPU implementations for both methods, with the largest performance increase for wide-beam systems. The fastest occlusion modeling algorithm was found to be GPU-accelerated ray-tracing over the majority of scene scales tested, which was found to be up to 2 times faster than the parallel CPU convex-hull implementation. Although GPU implementations of convex hull algorithms are not currently readily available, future development of GPU-accelerated convex-hull finding could make the new approach much more viable. However, in the meantime, ray-tracing is still preferable, since it has higher accuracy and can leverage existing implementations for high performance computing architectures for better performance