This paper describes a method for inferring three-dimensional (3D) plant branch structures that are hidden under leaves from multi-view observations. Unlike previous geometric approaches that heavily rely on the visibility of the branches or use parametric branching models, our method makes statistical inferences of branch structures in a probabilistic framework. By inferring the probability of branch existence using a Bayesian extension of image-to-image translation applied to each of multi-view images, our method generates a probabilistic plant 3D model, which represents the 3D branching pattern that cannot be directly observed. Experiments demonstrate the usefulness of the proposed approach in generating convincing branch structures in comparison to prior approaches.
 State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming. Graphical abstractDisplay Omitted HighlightsAutomatic algorithm for generating crown representations from a single tree image.Compact image-based representation of dense tree crowns suitable for network streaming.Output-sensitive rendering for distant trees.Automatic generation of billboard clouds for detailed trees. We present a semi-automatic and efficient method for producing full polygonal models of range scanned trees, which are initially represented as sparse point clouds. First, a skeleton of the trunk and main branches of the tree is produced based on the scanned point clouds. Due to the unavoidable incompleteness of the point clouds produced by range scans of trees, steps are taken to synthesize additional branches to produce plausible support for the tree crown. Appropriate dimensions for each branch section are estimated using allometric theory. Using this information, a mesh is produced around the full skeleton. Finally, leaves are positioned, oriented and connected to nearby branches. Our process requires only minimal user interaction, and the full process including scanning and modeling can be completed within minutes. In this paper, we introduce a simple sketching method to generate a realistic 3D tree model from a single image. The user draws at least two strokes in the tree image: the first crown stroke around the tree crown to mark up the leaf region, the second branch stroke from the tree root to mark up the main trunk, and possibly few other branch strokes for refinement. The method automatically generates a 3D tree model including branches and leaves. Branches are synthesized by a growth engine from a small library of elementary subtrees that are pre-defined or built on the fly from the recovered visible branches. The visible branches are automatically traced from the drawn branch strokes according to image statistics on the strokes. Leaves are generated from the region bounded by the first crown stroke to complete the tree. We demonstrate our method on a variety of examples.  Reconstructing and rendering trees is a challenging problem due to the geometric complexity involved, and the inherent difficulties of capture. In this paper we propose a volumetric approach to capture and render trees with relatively sparse foliage. Photographs of such trees typically have single pixels containing the blended projection of numerous leaves branches and background. We show how we estimate opacity values on a recursive grid, based on alphamattes extracted from a small number of calibrated photographs of a tree. This data structure is then used to render billboards attached to the centers of the grid cells. Each billboard is assigned a set of view-dependent textures corresponding to each input view. These textures are generated by approximating coverage masks based on opacity and depth from the camera. Rendering is performed using a view-dependent texturing algorithm. The resulting volumetric tree structure has low polygon count, permitting interactive rendering of realistic 3D trees. We illustrate the implementation of our system on several different real trees, and show that we can insert the resulting model in virtual scenes. Trees, bushes, and other plants are ubiquitous in urban environments, and realistic models of trees can add a great deal of realism to a digital urban scene. There has been much research on modeling tree structures, but limited work on reconstructing the geometry of real-world trees -- even then, most works have focused on reconstruction from photographs aided by significant user interaction. In this paper, we perform active laser scanning of real-world vegetation and present an automatic approach that robustly reconstructs skelet al structures of trees, from which full geometry can be generated. The core of our method is a series of global optimizations that fit skelet al structures to the often sparse, incomplete, and noisy point data. A significant benefit of our approach is its ability to reconstruct multiple overlapping trees simultaneously without segmentation. We demonstrate the effectiveness and robustness of our approach on many raw scans of different tree varieties. We introduce a novel 3D tree skeletonization method from multiple images.Affine transformation and iterative tracking are added to PyrLK optical flow.Parallel voxel flooding is proposed for determining point cloud of single branch.L-System extraction algorithm for tree skeletons is proposed. Display Omitted 3D tree models are widely applied to construct large-scale virtual scenes. However, converting real trees into computer representation faces two main problems. One is the low quality of reconstructed point cloud. The other is that the skeletonization produces inaccurate results due to the complex structure of trees. We propose a novel pipeline to reconstruct automatically accurate 3D tree skeletons from sequential tree images. It involves three steps: (1) Pre-processing: an optical flow based feature-matching algorithm is proposed to acquire high-quality point clouds. (2) Shape-based parallel skeletonization: tree shape is taken into consideration to detect specific structure features of trees during parallel flooding. (3) Post-processing: an L-System extraction algorithm is applied on the skeletons and the lightweight representation is obtained to meet various requirements. The effectiveness of our approach is demonstrated through experiments. 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. Our computer modeling technique reproduces a tree's 3D volume and skeleton from instrumented photographs. The technique involves first constructing the skeleton (trunk and the major branches) of the tree, and then applying an L-system starting from this skeleton. L-systems are one of the better known procedural models in the graphics community, especially after their popularization by P. Prusinkiewicz and A. Lindenmayer (1990). We present a novel image-based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural-looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically-looking tree geometry from a sparse set of images. Our solution directly integrates 2D 3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.