Search

Approaches for leveraging real-time graphics, virtual production technologies to bring the visual richness, diversity and fidelity of bespoke teaching venues into the realm of teleconference-based, distanced learning. A variety of readily accessible tools and implementations are presented that dramatically enhance the experience of teaching and learning through common teleconferencing platforms.

In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g., fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance. Finally, we provide a new scheme for accelerated ray casting inside the voxelization.

In this article the theoretical and the methodical basics of teaching computer graphics in the system of the general education are covered. First of all, it concerns lessons of informatics, but the computer graphics has serious opportunities for its using in the course of training other disciplines, in particular at geometry lessons. Use of vector graphics in geometry promotes activation of cognitive activity of pupils. Use of raster graphics also can make an essential contribution to development of a technique of geometry training. Raster graphics is fundamentals of computer graphics. When studying technology of raster editors it is impossible to ignore its value for understanding of other types of computer graphics, in particular fractal graphics. Studying raster technologies, it is also necessary to pay attention to transformation of formats of files as in the future the number of formats will only increase.Use of computer graphics allow children, even without art abilities, to feel as a certain creator, to create artistic images and gives big opportunities for self-realization. And the most important thing is the use of graphic opportunities of the computer allows to increase the interest of pupils in the lessons and to stir up their cognitive activity.

Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements.

Accurate simulation of all the senses in virtual environments is a computationally expensive task. Visual saliency models have been used to improve computational performance for rendered content, but this is insufficient for multi-modal environments. This paper considers cross-modal perception and, in particular, if and how olfaction affects visual attention. Two experiments are presented in this paper. Firstly, eye tracking is gathered from a number of participants to gain an impression about where and how they view virtual objects when smell is introduced compared to an odourless condition. Based on the results of this experiment a new type of saliency map in a selective-rendering pipeline is presented. A second experiment validates this approach, and demonstrates that participants rank images as better quality, when compared to a reference, for the same rendering budget.

Most state-of-the-art compression algorithms use complex connectivity traversal and prediction schemes, which are not efficient enough for online compression of large meshes. In this paper we propose a scalable massively parallel approach for compression and decompression of large triangle meshes using the GPU. Our method traverses the input mesh in a parallel breadth-first manner and encodes the connectivity data similarly to the well known cut-border machine. Geometry data is compressed using a local prediction strategy. In contrast to the original cut-border machine, we can additionally handle triangle meshes with inconsistently oriented faces. Our approach is more than one order of magnitude faster than currently used methods and achieves competitive compression rates.

We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms. Inspired by existing path-guiding algorithms, our method learns an approximate representation of the scene's spatio-directional radiance field in an unbiased and iterative manner. To that end, we propose an adaptive spatio-directional hybrid data structure, referred to as SD-tree, for storing and sampling incident radiance. The SD-tree consists of an upper part-a binary tree that partitions the 3D spatial domain of the light field-and a lower part-a quadtree that partitions the 2D directional domain. We further present a principled way to automatically budget training and rendering computations to minimize the variance of the final image. Our method does not require tuning hyperparameters, although we allow limiting the memory footprint of the SD-tree. The aforementioned properties, its ease of implementation, and its stable performance make our method compatible with production environments. We demonstrate the merits of our method on scenes with difficult visibility, detailed geometry, and complex specular-glossy light transport, achieving better performance than previous state-of-the-art algorithms.

Visual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible before. Visual computing techniques provide novel insights in order to understand complex material systems of interest, which is demonstrated by strongly rising number of new approaches, publishing new techniques for materials analysis and simulation.

Data visualization is an effective tool for communicating the results of opinion surveys, epidemiological studies, statistics on consumer habits, etc. The graphical representation of data usually assists human information processing by reducing demands on attention, working memory, and long-term memory. It allows, among other things, a faster reading of the information (by acting on the forms, directions, colors…), the independence of the language (or culture), a better capture the attention of the audience, etc. Data that could be graphically represented may be structured or unstructured. The unstructured data, whose volume grows exponentially, often hide important and even vital information for society and companies. It, therefore, takes a lot of work to extract valuable information from unstructured data. If it is easier to understand a message through structured data, such as a table, than through a long narrative text, it is even easier to convey a message through a graphic than a table. In our opinion, it is often very useful to synthesize the unstructured data in the form of graphical representations. In this paper, we present an approach for processing unstructured data containing statistics in order to represent them graphically. This approach allows transforming the unstructured data into structured one which globally conveys the same countable information. The graphical representation of such a structured data is then obvious. This approach deals with both quantitative and qualitative data. It is based on Natural Language Processing Techniques and Text Mining. An application that implements this process is also presented in this paper.

Given a set of rectangles embedded in the plane, we consider the problem of adjusting the layout to remove all overlap while preserving the orthogonal order of the rectangles. The objective is to minimize the displacement of the rectangles. We call this problem MINIMUM-DISPLACEMENT OVERLAP REMOVAL (MDOR). Our interest in this problem is motivated by the application of displaying metadata of archaeological sites. Because most existing overlap removal algorithms are not designed to minimize displacement while preserving orthogonal order, we present and compare several approaches which are tailored to our particular usecase. We introduce a new overlap removal heuristic which we call REARRANGE. Although conceptually simple, it is very effective in removing the overlap while keeping the displacement small. Furthermore, we propose an additional procedure to repair the orthogonal order after every iteration, with which we extend both our new heuristic and PRISM, a widely used overlap removal algorithm. We compare the performance of both approaches with and without this order repair method. The experimental results indicate that REARRANGE is very effective for heterogeneous input data where the overlap is concentrated in few dense regions. published

We propose a novel algorithm for construction of bounding volume hierarchies BVHs for multi-core CPU architectures. The algorithm constructs the BVH by a divisive top-down approach using a progressively refined cut of an existing auxiliary BVH. We propose a new strategy for refining the cut that significantly reduces the workload of individual steps of BVH construction. Additionally, we propose a new method for integrating spatial splits into the BVH construction algorithm. The auxiliary BVH is constructed using a very fast method such as LBVH based on Morton codes. We show that the method provides a very good trade-off between the build time and ray tracing performance. We evaluated the method within the Embree ray tracing framework and show that it compares favorably with the Embree BVH builders regarding build time while maintaining comparable ray tracing speed.

Tone-mapping constitutes a key component within the field of high dynamic range (HDR) imaging. Its importance is manifested in the vast amount of tone-mapping methods that can be found in the literature, which are the result of an active development in the area for more than two decades. Although these can accommodate most requirements for display of HDR images, new challenges arose with the advent of HDR video, calling for additional considerations in the design of tone-mapping operators (TMOs). Today, a range of TMOs exist that do support video material. We are now reaching a point where most camera captured HDR videos can be prepared in high quality without visible artifacts, for the constraints of a standard display device. In this report, we set out to summarize and categorize the research in tone-mapping as of today, distilling the most important trends and characteristics of the tone reproduction pipeline. While this gives a wide overview over the area, we then specifically focus on tone-mapping of HDR video and the problems this medium entails. First, we formulate the major challenges a video TMO needs to address. Then, we provide a description and categorization of each of the existing video TMOs. Finally, by constructing a set of quantitative measures, we evaluate the performance of a number of the operators, in order to give a hint on which can be expected to render the least amount of artifacts. This serves as a comprehensive reference, categorization and comparative assessment of the state-of-the-art in tone-mapping for HDR video.

We present a real-time multi-view facial capture system facilitated by synthetic training imagery. Our method is able to achieve high-quality markerless facial performance capture in real-time from multi-view helmet camera data, employing an actor specific regressor. The regressor training is tailored to specified actor appearance and we further condition it for the expected illumination conditions and the physical capture rig by generating the training data synthetically. In order to leverage the information present in live imagery, which is typically provided by multiple cameras, we propose a novel multi-view regression algorithm that uses multi-dimensional random ferns. We show that higher quality can be achieved by regressing on multiple video streams than previous approaches that were designed to operate on only a single view. Furthermore, we evaluate possible camera placements and propose a novel camera configuration that allows to mount cameras outside the field of view of the actor, which is very beneficial as the cameras are then less of a distraction for the actor and allow for an unobstructed line of sight to the director and other actors. Our new real-time facial capture approach has immediate application in on-set virtual production, in particular with the ever-growing demand for motion-captured facial animation in visual effects and video games.

The most common solutions to the light transport problem rely on either Monte Carlo MC integration or density estimation methods, such as uni- & bi-directional path tracing or photon mapping. Recent gradient-domain extensions of MC approaches show great promise; here, gradients of the final image are estimated numerically instead of the image intensities themselves with coherent paths generated from a deterministic shift mapping. We extend gradient-domain approaches to light transport simulation based on density estimation. As with previous gradient-domain methods, we detail important considerations that arise when moving from a primal- to gradient-domain estimator. We provide an efficient and straightforward solution to these problems. Our solution supports stochastic progressive density estimation, so it is robust to complex transport effects. We show that gradient-domain photon density estimation converges faster than its primal-domain counterpart, as well as being generally more robust than gradient-domain uni- & bi-directional path tracing for scenes dominated by complex transport.

We present a spatial index structure to accelerate ray tracing on GPUs. It is a flat, non-hierarchical spatial subdivision of the scene into axis aligned cells of varying size. In order to construct it, we first nest an octree into each cell of a uniform grid. We then apply two optimization passes to increase ray traversal performance: First, we reduce the expected cost for ray traversal by merging cells together. This adapts the structure to complex primitive distributions, solving the "teapot in a stadium" problem. Second, we decouple the cell boundaries used during traversal for rays entering and exiting a given cell. This allows us to extend the exiting boundaries over adjacent cells that are either empty or do not contain additional primitives. Now, exiting rays can skip empty space and avoid repeating intersection tests. Finally, we demonstrate that in addition to the fast ray traversal performance, the structure can be rebuilt efficiently in parallel, allowing for ray tracing dynamic scenes.

We propose a novel motion retargeting method that efficiently estimates artist-friendly rig space parameters. Inspired by the workflow typically observed in keyframe animation, our approach transfe...

Kljub tehnološkemu napredku zadnjih stoletij in desetletij se še vedno soočamo s problematiko prikaza in upodobitve barve v različnih medijih in ohranjanja zaznave barve. Ena od možnosti, za katero se lahko odločimo pri zagotavljanju stalne barvne zaznave, so modeli barvnega zaznavanja. Trenutno je aktualen CIECAM02, ki se še vedno ne uporablja v 3D računalniški grafi ki, s katero se vsak dan srečujemo. Namen raziskave je bil pregled barvnih prostorov v 3D računalniški grafiki, pregled reprodukcije barv in materialov, algoritmov za senčenje ter izbranih sodobnih tehnologij upodabljanja za doseganje korektne končne vizualizacije. V nadaljevanju smo želeli proučiti model barvnega zaznavanja CIECAM02 do te mere, da bi ga lahko uporabili v povezavi s 3D računalniško grafiko. V ta namen smo v programu Blender postavili testno sceno in jo upodobili s tremi upodobljevalniki: Blender Render in Cycles, ki sta že vgrajena, in z dodatkom Yafaray. Izkazalo se je, da CIECAM02 lahko uporabimo tudi v 3D prostoru in da z njegovo uporabo dobimo boljše rezultate ujemanja barv pri spremembi ozadja. Poleg tega smo ugotovili, da barv ne upodabljajo vsi upodobljevalniki enako. Omenjena raziskava je aktualna za vse, ki želijo svoje dvo- ali tridimezionalne izdelke predstaviti s pomočjo 3D računalniške grafike, torej tudi za področje vizualizacij oblačil in tekstilnih izdelkov, ki se uporabljajo pri modnem oblikovanju in oblikovanju interjerjev, avtomobilski, navtični in letalski industriji ter tudi širše, kjer so dovršene 3D vizualizacije tekstilij in oblačil nepogrešljivi element vizualnih in grafičnih komunikacij.

This paper presents a method for the reconnection of contour lines from scanned color images of topographical maps based on graphics processing unit (GPU) implementation. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. First, an effective method is presented for contour line reconnection from scanned topographical maps based on CPU. This method considers both the distance and direction between the two broken points of the contour lines. It gets better performance and has high connection rate, but the time complexity of the algorithm is nonlinear with the increasing size of topographical map. Second, the advantage of the massively parallel computing capability of GPU with the compute unified device architecture is taken to improve the algorithm. Finally, a better performance has been achieved based on the open source computer vision library. The experimental results show that the GPU implementation with loop-based patterns achieves a speedup of 1360× and the identical result compared with the implementation on CPU.

In this letter, an efficient Fortran-based out-ofgraphics processing unit (GPU) memory implementation of the method of moments (MoM) on heterogeneous low-cost single CPU/GPU computing platform is presented. The MoM solution process is based on the frequency-domain integral equation formulation and employs a wire-grid approach to evaluate the radiation pattern of the glide path antenna system. To handle processor heterogeneity and balance the workload between the host and the device, a novel task scheduling strategy, both for the matrix assembly phase and the solution of the MoM matrix equation, is proposed. Using the CUDA-capable GTX 680 GPU from NVIDIA, a speedup ratio of about 1.44× is reported when compared to the classical hybrid CPU/GPU approach, and about 2.21× when compared to the multicore CPU implementation.

The computer graphics and computer vision communities have been working closely together in recent\ud years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media\ud around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the\ud Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing\ud tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data;\ud iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and\ud 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics\ud and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey\ud recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover\ud research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest\ud possible further research directions.

Visualizing very large graphs by edge bundling is a promising method, yet subject to several challenges: speed, clutter, level-of-detail, and parameter control. We present CUBu, a framework that addresses the above problems in an integrated way. Fully GPU-based, CUBu bundles graphs of up to a million edges at interactive framerates, being over 50 times faster than comparable state-of-the-art methods, and has a simple and intuitive control of bundling parameters. CUBu extends and unifies existing bundling techniques, offering ways to control bundle shapes, separate bundles by edge direction, and shade bundles to create a level-of-detail visualization that shows both the graph core structure and its details. We demonstrate CUBu on several large graphs extracted from real-life application domains.

This paper explains the concepts behind the programmable rendering pipeline, as opposed to the fixed-function pipeline, and how the former can help with creating stunning visuals without degrading performance. In order to demonstrate these concepts, a practical implementation of water simulation using OpenGL is presented and relevant experiments are conducted. Additionally, the execution results are compared and the speedup of using the programmable pipeline over the fixed-function pipeline, in the case of water simulation, are given.

Real-time rendering processes in computer graphics systems are often designed around the principles of linear projection (LP), that is, simulating linear light rays passing through 3D space and intersecting a two-dimensional surface in order to create an image.

The proposed interactive crowd-behavior learning algorithms can be used to analyze crowd videos for surveillance and training applications. The authors' formulation combines online tracking algorithms from computer vision, nonlinear pedestrian motion models from computer graphics, and machine learning techniques to automatically compute trajectory-level pedestrian behaviors for each agent in the video. These learned behaviors are used to automatically detect anomalous behaviors, perform motion segmentation, and generate realistic behaviors for virtual reality training applications.

We propose a unified rendering approach that jointly handles motion and defocus blur for transparent and opaque objects at interactive frame rates. Our key idea is to create a sampled representation of all parts of the scene geometry that are potentially visible at any point in time for the duration of a frame in an initial rasterization step. We store the resulting temporally-varying fragments (t-fragments) in a bounding volume hierarchy which is rebuild every frame using a fast spatial median construction algorithm. This makes our approach suitable for interactive applications with dynamic scenes and animations. Next, we perform spatial sampling to determine all t-fragments that intersect with a specific viewing ray at any point in time. Viewing rays are sampled according to the lens uv-sampling for depth-of-field effects. In a final temporal sampling step, we evaluate the pre-determined viewing ray/t-fragment intersections for one or multiple points in time. This allows us to incorporate all standard shading effects including transparency. We describe the overall framework, present our GPU implementation, and evaluate our rendering approach with respect to scalability, quality, and performance.

Proceedings of Pacific Graphics 2016 (Okinawa); International audience; Digital landscape realism often comes from the multitude of details that are hard to model such as fallen leaves, rock piles orentangled fallen branches. In this article, we present a method for augmenting natural scenes with a huge amount of details suchas grass tufts, stones, leaves or twigs. Our approach takes advantage of the observation that those details can be approximatedby replications of a few similar objects and therefore relies on mass-instancing. We propose an original structure, the GhostTile, that stores a huge number of overlapping candidate objects in a tile, along with a pre-computed collision graph. Detailsare created by traversing the scene with the Ghost Tile and generating instances according to user-defined density fields thatallow to sculpt layers and piles of entangled objects while providing control over their density and distribution.

We present an algorithm to accelerate resolution independent curve rendering on mobile GPUs. Recent trends in graphics hardware have created a plethora of compressed texture formats specific to GPU manufacturers. However, certain implementations of platform independent path rendering require generating grayscale textures on the CPU containing the extent that each pixel is covered by the curve. In this paper, we demonstrate that generating a compressed grayscale texture prior to uploading it to the GPU creates faster rendering times in addition to the memory savings. We implement a real-time compression technique for coverage masks and compare our results against the GPU-based implementation of the highly optimized Skia rendering library. We also analyze the worst case properties of our compression algorithms. We observe up to a 2 $\times$ speed improvement over the existing GPU-based methods in addition to up to a 9:1 improvement in GPU memory gains. We demonstrate the performance on multiple mobile platforms.

A detail-on-demand scheme can alleviate both memory and GPU pressure on mobile devices caused by volume rendering. This approach allows a user to explore an entire dataset at its native resolution while simultaneously constraining the texture size being rendered to a dimension that does not exceed the processing capabilities of a portable device. This scheme produces higher-quality, more focused images rendered at interactive frame rates, while preserving the native resolution of the dataset.

This paper proposes new models of GPU energy consumption from the perspectives of hardware architects and graphics programmers by performing an architecture-independent analysis of the classical graphics rendering pipeline which is still in widespread use today. The detailed analysis includes graphics rendering workload, memory bandwidth and energy consumption . Although the models are derived from classical 3D pipeline, they are extensible to programmable pipelines. There are many factors that affect the performance and energy consumption of 3D graphics rendering, such as the number of textures, vertex sharing, level of details, and rendering algorithms. The proposed models are validated by our simulation study and used to guide our 3D graphics hardware design and 3D graphics programming in order to optimize performance and energy consumption of our GPU prototypes which have been successfully fabricated in SMIC 0.13m CMOS technology. HighlightsNew hardware-independent GPU energy models are proposed and verified with simulation.The analysis makes no assumptions about any specific hardware architecture.The models are established for every major state of a graphics pipeline.The models match with the simulation results very well.

With ever-increasing display resolution for wide field-of-view displays---such as head-mounted displays or 8k projectors---shading has become the major computational cost in rasterization. To reduce computational effort, we propose an algorithm that only shades visible features of the image while cost-effectively interpolating the remaining features without affecting perceived quality. In contrast to previous approaches we do not only simulate acuity falloff but also introduce a sampling scheme that incorporates multiple aspects of the human visual system: acuity, eye motion, contrast (stemming from geometry, material or lighting properties), and brightness adaptation. Our sampling scheme is incorporated into a deferred shading pipeline to shade the image's perceptually relevant fragments while a pull-push algorithm interpolates the radiance for the rest of the image. Our approach does not impose any restrictions on the performed shading. We conduct a number of psycho-visual experiments to validate scene- and task-independence of our approach. The number of fragments that need to be shaded is reduced by 50% to 80%. Our algorithm scales favorably with increasing resolution and field-of-view, rendering it well-suited for head-mounted displays and wide-field-of-view projection.

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

In this report, we review the current state of the art of web-based visualization applications. Recently, an increasing number of web-based visualization applications have emerged. This is due to the fact that new technologies offered by modern browsers greatly increased the capabilities for visualizations on the web. We first review these technical aspects that are enabling this development. This includes not only improvements for local rendering like WebGL and HTML5, but also infrastructures like grid or cloud computing platforms. Another important factor is the transfer of data between the server and the client. Therefore, we also discuss advances in this field, for example methods to reduce bandwidth requirements like compression and other optimizations such as progressive rendering and streaming. After establishing these technical foundations, we review existing web-based visualization applications and prototypes from various application domains. Furthermore, we propose a classification of these web-based applications based on the technologies and algorithms they employ. Finally, we also discuss promising application areas that would benefit from web-based visualization and assess their feasibility based on the existing approaches.

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high-resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well-suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high-quality soft shadows and dynamic lights moving on fixed-trajectories.

Voxel-based approaches are today's standard to encode volume data. Recently, directed acyclic graphs (DAGs) were successfully used for compressing sparse voxel scenes as well, but they are restricted to a single bit of (geometry) information per voxel. We present a method to compress arbitrary data, such as colors, normals, or reflectance information. By decoupling geometry and voxel data via a novel mapping scheme, we are able to apply the DAG principle to encode the topology, while using a palette-based compression for the voxel attributes, leading to a drastic memory reduction. Our method outperforms existing state-of-the-art techniques and is well-suited for GPU architectures. We achieve real-time performance on commodity hardware for colored scenes with up to 17 hierarchical levels (a 128K3 voxel resolution), which are stored fully in core.

In 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.

When the micro lens array machined by an ultra-precision machine was used in 3D computer graphics, a novel format of depth map image was invented with adaptively variable data length for multi requirements of different 3D computer graphics applications. A depth map is an image or image channel that contains information relating to the distance of the surfaces of objects a scene from a viewpoint. Depth maps can be applied for many functions: defocusing, rendering of 3D scenes, shadow mapping and other distance information-related applications.

In this paper, a graphics processing unit (GPU) based matching technique is proposed to perform fast feature matching between different images under various image conditions with viewpoint changes. Most recently, general-purpose graphics processing units (GPGPUs or GPUs) have become commonplace within high performance supercomputers. GPUs allow developers to effectively exploit the computational power for high performance computing. This paper focuses on improving the performance of feature matching based on self-organizing map by porting it onto the GPUs. GPU optimization has been applied for the fast computation of keypoints to make the system fast and efficient. This scheme has enhanced the overall performance and is much more efficient compared to other methods without degradation of detection results. The proposed matching algorithm is partitioned between the CPU and GPU in a way that best exploits the parallelism and perform matching between the different images. Experimental results demonstrate that fast feature matching is achieved using the graphics processing units, and its computational efficiency is checked by comparing its results and run times with those of some standard software (MATLAB) run on central processing unit (CPU).