In this paper, we describe a novel procedural modeling technique for generating realistic plant models from multi-view photographs. The realism is enhanced via visual and spatial information acquired from images. In contrast to previous approaches that heavily rely on user interaction to segment plants or recover branches in images, our method automatically estimates an accurate depth map of each image and extracts a 3D dense point cloud by exploiting an efficient stereophotogrammetry approach. Taking this point cloud as a soft constraint, we fit a parametric plant representation to simulate the plant growth progress. In this way, we are able to synthesize parametric plant models from real data provided by photos and 3D point clouds. We demonstrate the robustness of the proposed approach by modeling various plants with complex branching structures and significant self-occlusions. We also demonstrate that the proposed framework can be used to reconstruct ground-covering plants, such as bushes and shrubs which have been given little attention in the literature. The effectiveness of our approach is validated by visually and quantitatively comparing with the state-of-the-art approaches.

Abstract We present a level of detail (LOD) method designed for tree branches. It can be combined with methods for processing tree foliage to facilitate navigation through large virtual forests. Starting from a skeletal representation of a tree, we fit polygon meshes of various densities to the skeleton while the mesh density is adjusted according to the required visual fidelity. For distant models, these branch meshes are gradually replaced with semi‐transparent lines until the tree recedes to a few lines. Construction of these complete LOD models is guided by error metrics to ensure smooth transitions between adjacent LOD models. We then present an instancing technique for discrete LOD branch models, consisting of polygon meshes plus semi‐transparent lines. Line models with different transparencies are instanced on the GPU by merging multiple tree samples into a single model. Our technique reduces the number of draw calls in GPU and increases rendering performance. Our experiments demonstrate that large‐scale forest scenes can be rendered with excellent detail and shadows in real time.

Abstract Plants are important objects in virtual environments. High complexity of shape structure is found in plant communities. Level of detail (LOD) of plant geometric models becomes important for interactive forest rendering. We emphasize three major problems in current research: the time consumption in LOD model construction and extraction, the balance between visual effect and data compression, and the time consumption in the communication between Central Processing Unit (CPU) and Graphics Processing Unit (GPU). We present a new foliage simplification framework for LOD model and forest rendering. By an uneven subdivision of the tree crown volume, the cost for LOD model construction is drastically reduced. With a GPU‐oriented design of LOD storage structure for foliage, the costly hierarchical traversal of a binary tree is replaced by a sequential lookup of an array. The structure also decreases the communication between the CPU and the GPU in rendering. In addition, Leaf density is introduced to adapt compression to the local distribution of leaves, so that more visually relevant details are kept. According to foliage nature (broad leaves or needles), higher compression are finally reached using mixed polygon/line models. This framework is implemented on virtual scenes of simulated trees with high detail. Copyright © 2009 John Wiley & Sons, Ltd.

In this work, we propose a preliminary study on objective approaches in evaluating the synthesized 3D plants from the given image data. Two measures, in a general name of "resemblance index (RI)", are defined and investigated based on the normalized mutual information (RINI), and coincident-bit-counting criterion (RICBC), respectively. We propose a strategy of hierarchical evaluations on different attributes of objects. In this study, we only consider the geometrical and crown distribution attributes. The numerical investigations confirm the approach to be useful for a fast evaluation of 3D reconstructed plants/trees from image data. Discussions are given about the two measures based on the numerical examples.

Some very impressive results have been obtained in the past few years in plants and trees image synthesis. Some algorithms are largely based on the irregularity and fuzziness of the objects, and use fractals, graftals or particle systems. Others focus on the branching pattern of the trees with emphasis on morphology. Our concern here is the faithfulness of the models to the botanical nature of trees and plants. We present a model which integrates botanical knowledge of the architecture of the trees: how they grow, how they occupy space, where and how leaves, flowers or fruits are located, etc. The very first interest of the model we propose is its great richness: the same procedural methods can produce "plants" as different as weeping willows, fir trees, cedar trees, frangipani trees, poplars, pine trees, wild cherry trees, herbs, etc. Another very important benefit one can derive from the model is the integration of time which enables viewing the aging of a tree (possibility to get different pictures of the same tree at different ages, accurate simulation of the death of leaves and branches for example). The ease to integrate physical parameters such as wind, the incidence of factors such as insects attacks, use of fertilizers, plantation density, and so on makes it a useful tool for agronomy or botany.