Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.

This paper presents a new technique for modification of 3D terrains by hydraulic erosion. It efficiently couples fluid simulation using a Lagrangian approach, namely the Smoothed Particle Hydrodynamics (SPH) method, and a physically-based erosion model adopted from an Eulerian approach. The eroded sediment is associated with the SPH particles and is advected both implicitly, due to the particle motion, and explicitly, through an additional velocity field, which accounts for the sediment transfer between the particles. We propose a new donor-acceptor scheme for the explicit advection in SPH. Boundary particles associated to the terrain are used to mediate sediment exchange between the SPH particles and the terrain itself. Our results show that this particle-based method is efficient for the erosion of dense, large, and sparse fluid. Our implementation provides interactive results for scenes with up to 25,000 particles.

Abstract

We present an important step towards the solution of the problem of inverse procedural modeling by generating

Procedural methods present one of the most powerful techniques for authoring a vast variety of computer graphics models. However, their massive applicability is hindered by the lack of control and a low predictability of the results. In the classical procedural modeling pipeline, the user usually defines a set of rules, executes the procedural system, and by examining the results attempts to infer what should be changed in the system definition in order to achieve the desired output. We present guided procedural modeling, a new approach that allows a high level of top-down control by breaking the system into smaller building blocks that communicate. In our work we generalize the concept of the environment. The user creates a set of guides. Each guide defines a region in which a specific procedural model operates. These guides are connected by a set of links that serve for message passing between the procedural models attached to each guide. The entire model consists of a set of guides with procedural models, a graph representing their connection, and the method in which the guides interact. The modeling process is performed by modifying each of the described elements. The user can control the high-level description by editing the guides or manipulate the low-level description by changing the procedural rules. Changing the connectivity allows the user to create new complex forms in an easy and intuitive way. We show several examples of procedural structures, including an ornamental pattern, a street layout, a bridge, and a model of trees. We also demonstrate interactive examples for quick and intuitive editing using physics-based mass-spring system.