Procedural feature generation for volumetric terrains using voxel grammars

© 2018 Terrain generation is a fundamental requirement of many computer graphics simulations, including computer games, flight simulators and environments in feature films. There has been a considerable amount of research in this domain, which ranges between fully automated and semi-automated methods. Voxel representations of 3D terrains can create rich features that are not found in other forms of terrain generation techniques, such as caves and overhangs. In this article, we introduce a semi-automated method of generating features for volumetric terrains using a rule-based procedural generation system. Features are generated by selecting subsets of a voxel grid as input symbols to a grammar, composed of user-created operators. This results in overhangs and caves generated from a set of simple rules. The feature generation runs on the CPU and the GPU is utilised to extract a robust mesh from the volumetric dataset

A Survey of Procedural Techniques for City Generation

The computer game industry requires a skilled workforce and this combined with the complexity of modern games, means that production costs are extremely high. One of the most time consuming aspects is the creation of game geometry, the virtual world which the players inhabit. Procedural techniques have been used within computer graphics to create natural textures, simulate special effects and generate complex natural models including trees and waterfalls. It is these procedural techniques that we intend to harness to generate geometry and textures suitable for a game situated in an urban environment. Procedural techniques can provide many benefits for computer graphics applications when the correct algorithm is used. An overview of several commonly used procedural techniques including fractals, L-systems, Perlin noise, tiling systems and cellular basis is provided. The function of each technique and the resulting output they create are discussed to better understand their characteristics, benefits and relevance to the city generation problem. City generation is the creation of an urban area which necessitates the creation of buildings, situated along streets and arranged in appropriate patterns. Some research has already taken place into recreating road network patterns and generating buildings that can vary in function and architectural style. We will study the main body of existing research into procedural city generation and provide an overview of their implementations and a critique of their functionality and results. Finally we present areas in which further research into the generation of cities is required and outline our research goals for city generation

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Realization and Extension of the Xfrog Approach for Plant Modelling in the Graph-Grammar Based Language XL

Two well-known approaches for modelling virtual vegetation are grammar-based methods (L-systems) and the Xfrog method, which is based on graph transformations expanding "multiplier" nodes. We show that both approaches can be unified in the framework of "relational growth grammars", a variant of parallel graph grammars. We demonstrate this possibility and the synergistic benefits of the combination of both methods at simple plant models which were processed using our open-source software GroIMP

Visualization, Adaptation, and Transformation of Procedural Grammars

Procedural shape grammars are powerful tools for the automatic generation of highly detailed 3D content from a set of descriptive rules. It is easy to encode variations in stochastic and parametric grammars, and an uncountable number of models can be generated quickly. While shape grammars offer these advantages over manual 3D modeling, they also suffer from certain drawbacks. We present three novel methods that address some of the limitations of shape grammars. First, it is often difficult to grasp the diversity of models defined by a given grammar. We propose a pipeline to automatically generate, cluster, and select a set of representative preview images for a grammar. The system is based on a new view attribute descriptor that measures how suitable an image is in representing a model and that enables the comparison of different models derived from the same grammar. Second, the default distribution of models in a stochastic grammar is often undesirable. We introduce a framework that allows users to design a new probability distribution for a grammar without editing the rules. Gaussian process regression interpolates user preferences from a set of scored models over an entire shape space. A symbol split operation enables the adaptation of the grammar to generate models according to the learned distribution. Third, it is hard to combine elements of two grammars to emerge new designs. We present design transformations and grammar co-derivation to create new designs from existing ones. Algorithms for fine-grained rule merging can generate a large space of design variations and can be used to create animated transformation sequences between different procedural designs. Our contributions to visualize, adapt, and transform grammars makes the procedural modeling methodology more accessible to non-programmers