Quick-VDR: out-of-core view-dependent rendering of gigantic models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.     

Processing and interactive editing of huge point clouds from 3D scanners

This paper describes a new out-of-core multi-resolution data structure for real-time visualization, interactive editing and externally efficient processing of large point clouds. We describe an editing system that makes use of the novel data structure to provide interactive editing and preprocessing tools for large scanner data sets. Using the new data structure, we provide a complete tool chain for 3D scanner data processing, from data preprocessing and filtering to manual touch-up and real-time visualization. In particular, we describe an out-of-core outlier removal and bilateral geometry filtering algorithm, a toolset for interactive selection, painting, transformation, and filtering of huge out-of-core point-cloud data sets and a real-time rendering algorithm, which all use the same data structure as storage backend. The interactive tools work in real-time for small model modifications. For large scale editing operations, we employ a two-resolution approach where editing is planned in real-time and executed in an externally efficient offline computation afterwards. We evaluate our implementation on example data sets of sizes up to 63 GB, demonstrating that the proposed technique can be used effectively in real-world applications.     

An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees

We present an efficient technique for out-of-core multi-resolution construction and high quality interactive visualization of massive point clouds. Our approach introduces a novel hierarchical level of detail (LOD) organization based on multi-way kd-trees, which simplifies memory management and allows control over the LOD-tree height. The LOD tree, constructed bottom up using a fast high-quality point simplification method, is fully balanced and contains all uniformly sized nodes. To this end, we introduce and analyze three efficient point simplification approaches that yield a desired number of high-quality output points. For constant rendering performance, we propose an efficient rendering-on-a-budget method with asynchronous data loading, which delivers fully continuous high quality rendering through LOD geo-morphing and deferred blending. Our algorithm is incorporated in a full end-to-end rendering system, which supports both local rendering and cluster-parallel distributed rendering. The method is evaluated on complex models made of hundreds of millions of point samples.     

大型网格模型多分辨率的外存构建与交互绘制

结合多分辨率、网格排布和基于视点的绘制技术,提出一种外存多分辨率构建和绘制算法.采用适应性八叉树对模型的包围盒进行划分,自顶向下构建模型的多分辨率层次结构,较好地保持了原模型的细节分布;并对多分辨率结构中每个节点所包含的三角形片段进行网格排布优化,降低了缓存的平均失效率;在实时绘制时,采用基于视点的细节层次选择策略进行模型的细化;最后通过引入数据预取机制来隐藏磁盘I/O延时,进一步提高绘制性能.实验结果表明,该算法在绘制速度与细节保留上均优于同类MRMM算法.     

A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets

We present an adaptive out-of-core technique for rendering massive scalar volumes employing single-pass GPU ray casting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless ray casting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-gigavoxel datasets on a desktop PC.     

State-of-the-art in Large-Scale Volume Visualization Beyond Structured Data

Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state-of-the-art report, we review works focusing on large-scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out-of-core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever-increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large-scale volume rendering systems and also include a review of tools that support the various volume data types discussed.     

视相关大规模三维地形实时绘制技术综述

大规模三维地形表面实时绘制一直是国内外的研究热点.综述了大规模三维地形表面实时绘制算法的分类,包括基于规则网格和非规则网格、基于in-core和out-of-core、基于CPU和GPU的三大类绘制算法;阐述了大规模三维地形表面实时绘制算法的发展现状和关键技术,通过out-of-core算法的数据预取策略解决大数据量模型的交互式渲染问题,采用视点相关的多分辨率技术简化整个场景的复杂度,减少绘制的数据量;指出了各种方法的优点与不足,最后对大规模三维地形表面实时绘制所需要研究的问题进行了总结.     

Real-time single scattering inside inhomogeneous materials

In this paper we propose a novel technique to perform real-time rendering of translucent inhomogeneous materials, one of the most well-known problems of computer graphics. The developed technique is based on an adaptive volumetric point sampling, done in a preprocessing stage, which associates to each sample the optical depth for a predefined set of directions. This information is then used by a rendering algorithm that combines the object’s surface rasterization with a ray tracing algorithm, implemented on the graphics processor, to compose the final image. This approach allows us to simulate light scattering phenomena for inhomogeneous isotropic materials in real time with an arbitrary number of light sources. We tested our algorithm by comparing the produced images with the result of ray tracing and showed that the technique is effective.     

网络环境下基于外存的大规模流程工厂模型交互绘制

现有的基于网络的远程绘制系统在绘制过程中对网络带宽和时延具有较强的依赖性,为了获得较高的绘制速度,需要耗费大量的预处理时间和存储空间.针对网络环境下模型的设计校审工作对预处理时间、绘制速度和图像质量的实际需求,提出一种基于外存的大规模流程工厂模型交互绘制算法.绘制前,首先从服务器端获取模型的几何参数和拓扑信息;然后根据流程工厂模型特征,在客户端以设备和管线为基本单位组织外存数据,采用体元合并的方法快速完成模型层次细节的计算和存储.分析了校审内容和校审人员的运动习惯,并将其与基于视点可见性的预取算法相结合,在本地实现外存数据的高效预取,且绘制过程中无需传输模型面片信息.实验结果表明,文中方法在普通PC机上能够将具有21 M左右面片模型的预处理时间控制在5 min以内,在保证校审所需图像质量的前提下取得平均30帧/s的平稳帧速,且绘制过程不依赖网络带宽和时延.     

R-LODs: fast LOD-based ray tracing of massive models

We present a novel LOD (level-of-detail) algorithm to accelerate ray tracing of massive models. Our approach computes drastic simplifications of the model and the LODs are well integrated with the kd-tree data structure. We introduce a simple and efficient LOD metric to bound the error for primary and secondary rays. The LOD representation has small runtime overhead and our algorithm can be combined with ray coherence techniques and cache-coherent layouts to improve the performance. In practice, the use of LODs can alleviate aliasing artifacts and improve memory coherence. We implement our algorithm on both 32-bit and 64-bit machines and are able to achieve up to 2–20 times improvement in frame rate of rendering models consisting of tens or hundreds of millions of triangles with little loss in image quality.     

Layered point clouds: a simple and efficient multiresolution structure for distributing and rendering gigantic point-sampled models

We recently introduced an efficient multiresolution structure for distributing and rendering very large point sampled models on consumer graphics platforms [1]. The structure is based on a hierarchy of precomputed object-space point clouds, that are combined coarse-to-fine at rendering time to locally adapt sample densities according to the projected size in the image. The progressive block based refinement nature of the rendering traversal exploits on-board caching and object based rendering APIs, hides out-of-core data access latency through speculative prefetching, and lends itself well to incorporate backface, view frustum, and occlusion culling, as well as compression and view-dependent progressive transmission. The resulting system allows rendering of complex out-of-core models at high frame rates (over 60 M rendered points/second), supports network streaming, and is fundamentally simple to implement. We demonstrate the efficiency of the approach on a number of very large models, stored on local disks or accessed through a consumer level broadband network, including a massive 234 M samples isosurface generated by a compressible turbulence simulation and a 167 M samples model of Michelangelo's St. Matthew. Many of the details of our framework were presented in a previous study. We here provide a more thorough exposition, but also significant new material, including the presentation of a higher quality bottom-up construction method and additional qualitative and quantitative results.     

基于Marching Cubes重组的外存模型渐进压缩

外存模型是指其规模远远超出内存容量的海量模型．为提高其存储、传输、显示等操作的效率，对外存模型进行渐进式的压缩是非常重要的．但当前已有的外存模型压缩算法都是单一层次的，不能做到渐进压缩．为此，该文提出一种针对外存模型的渐进压缩方法．能高效地压缩外存模型，并进行多分辨率的传输和显示．该方法首先将外存模型的包围盒空间按照八叉树形式进行剖分和层次化组织，使得最精细层次的各个立方块空间中的局部模型都能完全装入内存进行处理；然后，在各个立方块中对局部的模型进行基于Marching Cubes方式的重新拟合，并在此基础上建立各个局部的自适应八叉树；最后，基于各个局部自适应的八叉树，由粗至细渐进地遍历全局自适应八叉树的各个节点，并利用对内存模型能高效渐进压缩编码的先进方法进行编码压缩．实验表明，该方法对外存模型的压缩比达到了与处理内存模型相似的压缩比．高于目前的外存模型压缩方法，是第一个能渐进压缩外存模型的方法．     

Out-of-Core and Dynamic Programming for Data Distribution on a Volume Visualization Cluster

Ray directed volume-rendering algorithms are well suited for parallel implementation in a distributed cluster environment. For distributed ray casting, the scene must be partitioned between nodes for good load balancing, and a strict view-dependent priority order is required for image composition. In this paper, we define the load balanced network distribution (LBND) problem and map it to the NP-complete precedence constrained job-shop scheduling problem. We introduce a kd-tree solution and a dynamic programming solution. To process a massive data set, either a parallel or an out-of-core approach is required. Parallel preprocessing is performed by render nodes on data, which are allocated using a static data structure. Volumetric data sets often contain a large portion of voxels that will never be rendered, or empty space. Parallel preprocessing fails to take advantage of this. Our   slab-projection slice, introduced in this paper, tracks empty space across consecutive slices of data to reduce the amount of data distributed and rendered. It is used to facilitate out-of-core bricking and kd-tree partitioning. Load balancing using each of our approaches is compared with traditional methods using several segmented regions of the Visible Korean data set.     

Out-of-core adaptive iso-surface extraction from binary volume data

Volumetric datasets have already been used in multiple domains. Recent improvements in acquisition devices have boosted the size of available datasets. We present an out-of-core algorithm for iso-surface extraction from huge volumetric data. Our algorithm uses a divide and conquer approach that divides the volume and processes every meta-cell sequentially. We combine our approach with a dual surface extraction algorithm in order to build adaptive meshes. Our solution produces patches of adaptive meshes that can finally be combined to generate a manifold and closed surface. As our approach processes only a part of the volume in-core, with a minimum of redundancy, it can handle very big volumes by modifying the meta-cells size to fit to the in-core memory available. Moreover, our algorithm can be parallelized in order to boost processing times and increase its interactivity. We present examples of the application of our solution to huge segmented volumes.     

Interactive multiscale tensor reconstruction for multiresolution volume visualization

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.     

基于连续HLOD的大规模场景快速绘制算法

大规模场景的快速绘制是虚拟现实技术重要的研究课题之一.为了加速场景的绘制,一般采用层次细节模型和可见性裁剪方法,但是现有算法在处理大规模场景时存在着局限性.本文提出了一种新的大规模场景快速绘制算法,该算法在场景层次划分的基础上,利用拓扑结构可变的网格简化方法为场景层次计算连续的分层层次细节模型（HLOD）;然后在实时绘制阶段,对场景分层层次细节模型进行视点相关的全局和局部细化,并结合快速有效的视域裁剪,从而大大加速了场景绘制速度.实验结果表明该算法是简单有效的,并且算法还可以进一步扩展到外存方式.     

Perception-based model simplification for motion blur rendering

Motion blur effects are important to motion perception in visual arts, interactive games and animation applications. Usually, such motion blur rendering is quite time consuming, thus blocking the online/interactive use of the effects. Motivated by the human perception in relation to moving objects, this paper presents simplified geometric models that enable to speedup motion blur rendering, which has not been tracked in motion blur rendering specifically. We develop a novel algorithm to simplify models with motion-aware, to preserve the features whose characteristics are perceivable in motion. We deduce the formula to outline the level of detail simplification by the object moving velocity. Using our simplified models, methods for motion blur rendering can achieve the rendering quality as using the original models, and obtain the processing acceleration mostly. The experimental results have shown the effectiveness of our approach, more acceleration with the larger models or faster motion (e.g. for the dragon model with over a million facets, the motion-blur rendering via hierarchical stochastic rasterization is sped up by over 27 times).     

基于LOD控制与内外存调度的大型三维点云数据绘制

通过结合基于视点的细节层次（level-of-detail，LOD）控制技术和内外存调度的数据控制策略，实现大型三维点云数据在一般配置PC机上的实时交互浏览．首先将输入点云分为大小相等的若干块。然后对每块数据分别建立误差控制下的多分辨率数据结构，并进行内外存分配．在交互绘制中，通过用户视点来确定当前的感兴趣区域，以控制模型表面的细节层次分布．该算法不但可以实现大型点云数据的实时交互绘制，而且可有效地提高一般点云数据绘制时的内存使用效率．     

KD-tree based parallel adaptive rendering

Multidimensional adaptive sampling technique is crucial for generating high quality images with effects such as motion blur, depth-of-field and soft shadows, but it costs a lot of memory and computation time. We propose a novel kd-tree based parallel adaptive rendering approach. First, a?two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and control the memory cost: in the prepare stage, we coarsely sample the entire multidimensional space and use kd-tree structure to separate it into several multidimensional subspaces; in the main stage, each subspace is refined by a sub kd-tree and rendered in parallel. Second, novel kd-tree based strategies are introduced to measure space’s error value and generate anisotropic Poisson disk samples. The experimental results show that our algorithm produces better quality images than previous ones.     

Error-controlled real-time cut updates for multi-resolution volume rendering

Multi-resolution techniques are required for rendering large volumetric datasets exceeding the size of the graphics card's memory or even the main memory. The cut through the multi-resolution volume representation is defined by selection criteria based on error metrics. For GPU-based volume rendering, this cut has to fit into the graphics card's memory and needs to be continuously updated due to the interaction with the volume such as changing the area of interest, the transfer function or the viewpoint. We introduce a greedy cut update algorithm based on split-and-collapse operations for updating the cut on a frame-to-frame basis. This approach is guided by a global data-based metric based on the distortion of classified voxel data, and it takes into account a limited download budget for transferring data from main memory into the graphics card to avoid large frame rate variations. Our out-of-core support for handling very large volumes also makes use of split-and-collapse operations to generate an extended cut in the main memory. Finally, we introduce an optimal polynomial-time cut update algorithm, which maximizes the error reduction between consecutive frames. This algorithm is used to verify how close to the optimum our greedy split-and-collapse algorithm performs.