基于设备性能的Web3D动态实时光影云渲染系统

本文面向多种硬件平台提出了一套Web3D实时动态光影的协同式渲染系统,该系统把Web前端的硬件性能作为整个云渲染系统中光影渲染任务分配的关键因素.对于Web前端性能较强的硬件设备,系统分配复杂度较高的光影渲染任务给前端,相应的云后端的渲染负担则有所降低;反之,系统则分配复杂度较低的光影渲染任务给前端,相应的云后端承担大...     

基于OSG的飞机3D维修多场景动态调度方法

以开源图形软件OpenSceneGraph作为底层渲染引擎,结合分页调度技术及多线程渲染策略,建立3D维护场景树,提出一种针对多个3D场景的动态调度方法,并使用多线程渲染方式对场景进行实时绘制,使各场景数据分批进入内存并实现实时卸载,改善内存使用率过高和场景绘制时帧速过低等关键问题。该方法应用于3D飞机维修仿真软件开发,与常规方法对比证明了这种方法的有效性。     

森林场景的钢笔风格化实时绘制技术*

在大型复杂场景绘制关键技术研究项目中,通过把复杂场景绘制与调度技术,如Slicing、MMI、层次图像缓存和基本布告板等大场景实时加速绘制技术的多层次绘制方法,融合到非真实感绘制技术中,以提高系统面对复杂场景的实时绘制能力。最后实现了对森林等大型场景的钢笔画绘制,借助非真实感绘制技术中基于图像空间的生成方法,利用各种不同大小及形状的笔画实现了艺术化森林场景实时交互绘制。提出了植物多层次钢笔风格化渲染算法。     

基于云端可视化交互的强化学习平台

强化学习是一个与环境交互的学习过程,在实验场景中,训练环境部署的可扩展性和算法验证的便捷性常受限于物理引擎和渲染模块的高耦合性。为对物理引擎和渲染模块进行解耦,构建一种面向物理引擎和渲染模块的云端交互式模型,其中包括操作字典、元素字典和对应的算法接口,并基于该模型实现模拟器。通过集成模拟器、可视化工具和知识管理等组件,搭建支持云端可视化交互的强化学习平台,并以MuJoCo物理引擎为例,验证Web模拟器接入自定义物理引擎的便捷性。实验和分析结果验证了该模型的有效性,其可方便接入平台,实现云端渲染并提高所属集群的利用率。     

物联网智能家居虚拟装配交互系统的设计与实现

在物联网智能家居设备装配过程中容易因误操作而造成设备损坏,甚至引起电网事故、人身伤亡事故、财产损失惨重等问题.为了解决这个问题,设计了物联网智能家居虚拟装配交互系统.该系统使用Unity3D实时渲染引擎进行三维场景展示,交互端则使用Unity3D的物理引擎模拟事件触发,选用具有行业标准的物联网数据库来控制智能家居的实时场景交互.通过多角度镜头和实时参数调节,用户在三维引擎操作端可实时查看智能家居安装的进度和交互效果.     

一种简化的流体方法快速仿真大型三维云场景

提出了一种改进的立体云模拟方法,用Navier-Stokes流体方程描述云的运动规律,同时考虑大气运动条件下云的特性,建立云的运动模型。在云的实时渲染方面,基于太阳光照方向和天气状况提出了一种简单的光照模型,大幅度地提高了云的渲染速度。此外,使用改进的环状Impostor技术来提高大范围云层的渲染速度。基于上述理论模型,利用实验室自主图形引擎开发了一套三维云仿真插件,广泛应用于各种实时仿真和科技娱乐项目中。该方法绘制的云具有规模大、真实感强、渲染速度快的特点。     

网络3D新时尚——CULT3D

1、Cult3D简介 Cult3D是由Cycore公司(http://www.cult3d.com)发布的全新的Web3D技术,一个跨平台的3D引擎,其目的是在网页上建立互动的3D物件。它具有一个纯软件(software based)、跨平台(multi—platform)的渲染引擎,因此不需要昂贵的3D图形加速设备就可以产生全运动的三维场景动画以及实时的交互控制,一次开发,在PC、MAC平台都能运用,用户只须安装相     

基于体绘制的动态云场景模拟

为了实现动态云场景的创建与绘制,文中提出一种基于体绘制的三维云模拟算法。算法用Perlin噪声干扰椭球模型,得到云场景区域内体素颜色的基本数据,然后对体素颜色进行软化模糊和添加阴影处理,建立绘制区域的云数据模型;同时通过ray casting对绘制区域进行体数据采样渲染,模拟出三维云的效果。仿真实验结果表明：该算法模拟出的云场景可让云形状颜色位置动态改变,也可让视点穿过云层,具有动态和交互效果,是一种可行的动态云场景模拟算法。     

大规模三维云实时模拟方法

针对虚拟环境中大规模三维云渲染开销大的问题,提出一种大规模三维云实时模拟方法.在云建模方面,利用Navier-Stokes流体力学公式模拟云的动态生成,提出一种基于八叉树的模型化简策略,减少了云模型粒子数;在渲染阶段,提出一种基于Cell的绘制更新策略,结合Impostor技术自动混合绘制三维云与Impostor,实现了大规模三维云的实时模拟.实验结果表明,该方法基于物理的方法模拟云,同时在绘制阶段根据视点的移动实时更新,效果逼真;与同类方法相比,基于Cell的绘制策略更新时计算量更小,有效地避免了绘制更新时常见的抖动和跳变问题.     

基于端边云协同和MIRF_WPSO的流程工艺参数自适应实时优化模型

针对流程工业生产过程中因工序间相互耦合、工艺数据量庞大且处理时延高而导致的工艺参数优化实时性难以保证的问题,提出一种基于端边云协同和MIRF_WPSO的流程工艺参数自适应实时优化模型.首先,基于边缘计算技术搭建多源异构流程工艺参数端边云协同实时优化架构;其次,构建基于互信息随机森林MIRF和自适应惯性权重粒子群WPSO的工艺参数优化算法MIRF_WPSO,并将MIRF_WPSO算法部署在边缘端以实现工艺参数的实时优化,同时通过在云端部署自更新机制来实现边缘端算法模型的自感知更新,从而形成集算法训练-更新-调用的端边云高效协同自动化闭环网络;最后,搭建实验平台,实验结果表明,“端-边-云”协同模式可以有效缓解云端计算压力,能够实时、高效地对流程工艺参数进行自优化调控,将质量指标平均标偏从1.86%降到1.25%,优化速度提高11.4%,为流程工业生产过程智能化进一步发展提供新的思路.     

基于线性八叉树的快速直接体绘制算法

提出了基于线性八叉树的加速体绘制算法.利用线性八叉树对物体进行空间剖分,光线投射法跨越体数据集中的空体素,以提高绘制的速度.针对光线穿越体数据时的特殊情况,改进线性八叉树邻域查找的方法,特别是不同尺寸的邻域查找方法,克服了层次八叉树邻域查找的低效率,同时提出了光线离开平面的简洁判定方法,方便光线下一个采样点的计算.实验结果表明,该算法能够有效地提高绘制的速度.     

真实感图形绘制技术研究

随着虚拟现实技术的发展,对各种图形绘制技术提出了越来越高的要求。好的图形绘制技术是虚拟现实、动态仿真、实时可视化、高品质动画等越来越多的图像、图形应用技术的实现基础。本文主要介绍了基于图像的绘制技术、基于点的绘制技术,以及基于图形与图像的混合绘制技术在原理和应用。     

Physically Based Real-Time Rendering of Teeth and Partial Restorations

Visually accurate real-time rendering of teeth has many applications ranging from computer games to dental computer aided design (CAD). Similar to skin, the realistic and physically correct appearance of teeth cannot be achieved by simply using opaque diffuse textures, mainly because of the subsurface scattering behaviours of both. While both have a layered structure in common, the scattering characteristics of the teeth layers are drastically different from those of the skin, making rendering much more complicated. We present an approach which uses the Henyey–Greenstein scattering to achieve a near realistic real-time rendering of human teeth. To simulate the multi-layered geometry of teeth, we use standardized teeth models with dentin cores and fit them to real scanned teeth or dental restorations. By using a proxy geometry to compute the scattering, we can also render partial restorations as they would look like when attached to the remaining teeth. Finally, we compare our results to the VITA shade systems and human teeth to evaluate the visual fidelity of our approach.     

A Semi-Procedural Convolutional Material Prior

Lightweight material capture methods require a material prior, defining the subspace of plausible textures within the large space of unconstrained texel grids. Previous work has either used deep neural networks (trained on large synthetic material datasets) or procedural node graphs (constructed by expert artists) as such priors. In this paper, we propose a semi-procedural differentiable material prior that represents materials as a set of (typically procedural) grayscale noises and patterns that are processed by a sequence of lightweight learnable convolutional filter operations. We demonstrate that the restricted structure of this architecture acts as an inductive bias on the space of material appearances, allowing us to optimize the weights of the convolutions per-material, with no need for pre-training on a large dataset. Combined with a differentiable rendering step and a perceptual loss, we enable single-image tileable material capture comparable with state of the art. Our approach does not target the pixel-perfect recovery of the material, but rather uses noises and patterns as input to match the target appearance. To achieve this, it does not require complex procedural graphs, and has a much lower complexity, computational cost and storage cost. We also enable control over the results, through changing the provided patterns and using guide maps to push the material properties towards a user-driven objective.     

Interactive Subsurface Scattering for Materials With High Scattering Distances

Existing algorithms for rendering subsurface scattering in real time cannot deal well with scattering over longer distances. Kernels for image space algorithms become very large in these circumstances and separation does not work anymore, while geometry-based algorithms cannot preserve details very well. We present a novel approach that deals with all these downsides. While for lower scattering distances, the advantages of geometry-based methods are small, this is not the case anymore for high scattering distances (as we will show). Our proposed method takes advantage of the highly detailed results of image space algorithms and combines it with a geometry-based method to add the essential scattering from sources not included in image space. Our algorithm does not require pre-computation based on the scene's geometry, it can be applied to static and animated objects directly. Our method is able to provide results that come close to ray-traced images which we will show in direct comparisons with images generated by PBRT. We will compare our results to state of the art techniques that are applicable in these scenarios and will show that we provide superior image quality while maintaining interactive rendering times.      

硬件支持下的实时毛发动画

长期以来，由于图形硬件提供的能力有限，毛发动画一直只能限制在非实时动画创作领域之中。现在，随着图形硬件技术的发展，特别是可编程图形硬件的出现，实时毛发动画技术已趋成熟，逐渐进入实用阶段。本文在回顾毛发动画技术发展历程的基础上，结合当前的图形硬件发展水平，从建模，渲染与动画三个方面分别论述了实现实时毛发动画的相关算法与技术。     

Decision trees for accelerating unimodal, hybrid and multimodal rendering models

This paper deals with the rendering of segmented unimodal, hybrid and aligned multimodal voxel models. We propose a data structure that classifies the segmented voxels into categories, so that whenever the model has to be traversed, only the selected categories are visited and the empty and non-selected voxels are skipped. This strategy is based on: (i) a decision tree, called the rendering decision tree (RDT), which represents the hierarchy of the classification process and (ii) an intermediate run-length encoding (RLE) of the classified voxel model. The traversal of the voxel model given a user query consists of two steps: first, the RDT is traversed and the set of selected categories computed; next, the RLE is visited, but the non-selected runs are skipped and only the voxels of the original model that are codified are accessed in selected runs of the RLE. This strategy has been used to render a voxel model by back-to-front traversal and splatting as well as to construct 3D textures for hardware-driven 3D texture mapping. The results show that the voxel model traversal is significantly accelerated.     

Real-Time Watercolor for Animation

We present algorithms that allow for real-time rendering of 3D-scenes with a watercolor painting appearance. Our approach provides an appropriate simplification of the visual complexity, imitates characteristic natural effects of watercolor, and provides two essential painting techniques: the wet-on-wet and the wet-on-dry painting. We concentrate on efficient algorithms based on image space processing rather than on an exact simulation. This allows for the real-time rendering of 3D-scenes. During an animation a high frame-to-frame coherence can be achieved due to a stable segmentation scheme. Finally, we seamlessly integrate a smooth illumination into the watercolor renderings using information from the 3D-scene.     

基于图像的铅笔画绘制技术的研究现状与展望

非真实感绘制(Non-Photorcalistic Rendcring,NPR)是计算机图形学领域的一个分支,旨在使用计算机来生成具有某种艺术效果的作品。目前,非真实感绘制技术已经能够成功地模拟油画、水彩画、卡通画、铅笔画等艺术风格,在许多领域得到很广泛的应用。铅笔素描是艺术造型的基础。作为一种最简单的艺术表现形式,铅笔画凭借其简洁流畅的特点深受广大艺术爱好者的喜爱。如今,基于图像的铅笔画绘制技术已经是NPR领域的一个重要组成部分,受到诸多研究人员的关注,但该技术仍处于成长阶段,算法的通用性还无法满足现实应用的需求。     

Interactive GPU-based adaptive cartoon-style rendering

In this paper we present a novel real-time cartoon-style rendering approach, which targets very large meshes. Cartoon drawing usually uses a limited number of colors for shading and emphasizes special effects, such as sharp curvature and silhouettes. It also paints the remaining large regions with uniform solid colors. Our approach quantizes light intensity to generate different shadow colors and utilizes multiresolution mesh hierarchy to maintain appropriate levels of detail across various regions of the mesh. To comply with visual requirements, our algorithm exploits graphics hardware programmability to draw smooth silhouette and color boundaries within the vertex and fragment processors. We have adopted a simplification scheme that executes simplification operators without incurring extra simplification operations as a precondition. The real-time refinement of the mesh, which is performed by the graphics processing unit (GPU), dramatically improves image quality and reduces CPU load.