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.

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Figure 1: A 3D model of a tree is imported (a). Our system automatically computes a dynamic model that is able to react interactively to environmental changes such as trees growing together (b) or when obstacles are moved towards the tree and cast shadow on it (c)-(e). AbstractWe present a dynamic tree modeling and representation technique that allows complex tree models to interact with their environment. Our method uses changes in the light distribution and proximity to solid obstacles and other trees as approximations of biologically motivated transformations on a skeletal representation of the tree's main branches and its procedurally generated foliage. Parts of the tree are transformed only when required, thus our approach is much faster than common algorithms such as Open L-Systems or space colonization methods. Input is a skeleton-based tree geometry that can be computed from common tree production systems or from reconstructed laser scanning models. Our approach enables content creators to directly interact with trees and to create visually convincing ecosystems interactively. We present different interaction types and evaluate our method by comparing our transformations to biologically based growth simulation techniques.

In this paper we utilize depth information to extend a line drawing algorithm, improving depth perception and object differentiation in large and spatially complex scenes. We consider different scales of features and apply a flow-based morphological filter to the scenes. Based on this two line drawing styles are defined. The proposed algorithm works in real-time and enables users to manipulate the parameter space through instant visual feedback. We evaluated the effectiveness of our method by performing a user study.

Figure 1: Our method creates abstract stylized objects from a given input model (left). We analyze the shape and its geometry to guide the stylization and abstraction of the object. Essentially, the user makes a selection from a prioritized list of style operands and applies it on the object. The stylized versions of the input can be rendered in various ways using non-photorealistic rendering.