Modeling Complex Unfoliaged Trees from a Sparse Set of Images
Modeling Complex Unfoliaged Trees from a Sparse Set of Images
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DOI：
10.1111/j.1467-8659.2010.01794.x
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发表时间：
2010-09
影响因子：
2.5
通讯作者：
L. D. Lopez;Yuanyuan Ding;Jingyi Yu
中科院分区：
文献类型：
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作者：
L. D. Lopez;Yuanyuan Ding;Jingyi Yu
我们提出了一种新颖的基于图像的技术来建模复杂的无叶树木。现有的树木建模工具要么需要捕获大量视图以进行密集 3D 重建，要么依赖用户输入和植物规则来合成看起来自然的树木几何形状。在本文中，我们专注于从一组稀疏图像中忠实地恢复真实的而不是逼真的树几何形状。我们的解决方案直接将 2D/3D 树拓扑作为形状先验集成到建模过程中。对于每个输入视图，我们首先从其遮罩图像估计 2D 骨架图，然后通过施加树拓扑从图中找到 2D 骨架树。我们开发了一种简单但有效的技术来计算与 2D 骨架最一致的最佳 3D 骨架树。对于 3D 骨架树中的每条边，我们进一步应用体积重建来恢复其相应的弯曲分支。最后，我们使用分段圆柱体来近似体积结果中的每个分支。我们在各种树上演示了我们的框架，以说明我们技术的稳健性和实用性。
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.