Rendering procedural textures for huge digital worlds

为巨大的数字世界渲染程序纹理

• 批准号: 431478017
• 负责人: Professor Dr.-Ing. Carsten Dachsbacher
• 金额: --
• 依托单位: Lehrstuhl für Computergrafik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/431478017?language=en
• 关键词: Rendering; procedural; textures; huge; digital

The ever increasing demands on realism and detail in virtual 3D scenes lead to a tremendous amount of data. One major driving force is textures which are widely used to represent fine visual details, such as variation of material parameters across surfaces or displacements. It is common that textures are obtained from procedural models which are well suited to create stochastic textures, e.g. to mimic natural phenomena. Procedural texturing is a generative approach where textures are compactly represented by a set of functions which are evaluated to produce the final texture. In this project we build on Procedural Texture Graphs (PTGs), which represent the generative process as a graph where source nodes are mathematical functions, inner nodes are pixel processing operations and sink nodes are the final output textures.In a typical production pipeline, textures are either computed upfront which becomes extremely storage demanding, or are evaluated on-the-fly during texture accesses resulting in many redundant calculations. Our project is concerned with this quandary. We plan to treat procedural texture synthesis and photo-realistic rendering as one tightly coupled entity to make the rendering of highly detailed scenes feasible using texture synthesis on demand -- and reduce the redundant calculations by novel caching schemes accounting for all aspects of the pipeline from texture evaluation to the needs of high-quality rendering. The latter requires texture filtering, i.e. computing the weighted average of texels in a subregion of the texture determined by ray differentials. We plan to address the multitude of challenges that comes along with this approach by first deriving a novel texture filtering theory for prefiltering and antialiasing of textures created from a PTG (storing color as well as normals/displacements). The key will be newly developed caching algorithms which exploit the additional knowledge that a PTG provides, namely how a texture evolves from individual basis functions and operations.The second challenge with procedural textures is, although powerful and flexible, the creation of textures with a desired look: the construction of an appropriate graph is tedious for artists and requires in-depth knowledge of the underlying operations. Our new approach requires to add technical metadata to the PTG, which would make the construction task even more difficult. To overcome this, we want to facilitate the production of the graph by a semi-automatic approach from input exemplars, which are often given for production rendering. We will develop new algorithms to extract a set of elementary functions and combination operators from the exemplars to obtain images with similar appearance. A fully automatic tool would be neither realistic neither relevant, because artists need to control the result. To this end, we aim at a feedback loop approach: artists fix constraints while the algorithms solve sub-problems.

在虚拟3D场景中，对真实感和细节的需求不断增加，导致了大量的数据。一个主要的驱动力是纹理，它被广泛用于表示精细的视觉细节，例如材料参数在表面或位移上的变化。通常，纹理是从程序模型中获得的，这些模型非常适合创建随机纹理，例如模拟自然现象。过程纹理是一种生成方法，其中纹理由一组函数紧凑地表示，这些函数被评估以产生最终纹理。在这个项目中，我们建立了程序纹理图（PTGs），它将生成过程表示为图形，其中源节点是数学函数，内部节点是像素处理操作，sink节点是最终输出纹理。在一个典型的生产管道中，纹理要么是预先计算的，这变得非常需要存储，要么是在纹理访问期间进行动态评估，导致许多冗余计算。我们的项目与这种困境有关。我们计划将程序纹理合成和照片真实感渲染作为一个紧密耦合的实体来处理，以便根据需要使用纹理合成来渲染高度详细的场景，并通过考虑从纹理评估到高质量渲染需求的管道的各个方面的新颖缓存方案来减少冗余计算。后者需要纹理滤波，即计算由射线微分确定的纹理子区域中的纹理的加权平均值。我们计划通过首先推导一种新的纹理过滤理论来解决这种方法带来的众多挑战，该理论用于从PTG（存储颜色以及法线/位移）创建的纹理的预滤波和抗锯齿。关键将是新开发的缓存算法，它利用PTG提供的额外知识，即纹理如何从单个基函数和操作演变。程序纹理的第二个挑战是，尽管功能强大且灵活，但具有理想外观的纹理的创建：对于艺术家来说，构建适当的图形是乏味的，并且需要深入了解底层操作。我们的新方法需要向PTG添加技术元数据，这将使构建任务更加困难。为了克服这个问题，我们希望通过输入示例的半自动方法来促进图形的生成，这些示例通常用于生产渲染。我们将开发新的算法，从样本中提取一组初等函数和组合算子，以获得具有相似外观的图像。全自动工具既不现实也不相关，因为美工需要控制结果。为此，我们的目标是一种反馈循环方法：艺术家修复约束，而算法解决子问题。