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.

中文翻译：

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从多个视图图像进行逼真的程序植物建模

在本文中，我们描述了一种用于从多视图照片生成逼真的植物模型的新颖过程建模技术。通过从图像获取的视觉和空间信息，增强了真实感。与以前严重依赖用户交互以分割植物或恢复图像中的分支的方法相比，我们的方法通过利用有效的立体摄影测量方法自动估计每个图像的准确深度图并提取3D密集点云。以该点云为软约束，我们拟合参数化植物表示来模拟植物生长进度。通过这种方式，我们能够从照片和3D点云提供的真实数据中合成参数化工厂模型。我们通过对具有复杂分支结构和显着自我遮挡的各种植物进行建模来证明所提出方法的鲁棒性。我们还证明了所提出的框架可用于重建地面覆盖的植物，例如灌木丛和灌木丛，这些文献在文献中很少受到关注。通过与最新技术进行视觉和定量比较，我们的方法的有效性得到了验证。