The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette‐defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.

中文翻译：

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基于图像的树变体

自动生成逼真的植被，密切再现特定植物物种的外观，仍然是计算机图形学中的一个具有挑战性的课题。在本文中，我们提出了一种新方法，可以从一小部分树木的正面 RGBA 图像集合中生成新的树木模型。新模型可以表示为单个广告牌（适用于建筑渲染等领域的静态图像生成）或广告牌云（在交互式应用程序中提供视差效果）。我们方法的关键组成部分包括通过示例countours的凸组合合成新轮廓，自动分割为冠/树干类别以及RGBA颜色从示例图像到合成目标的转移。我们还描述了一种全自动方法，通过提取超像素并将它们分布在轮廓定义的 3D 体积内，将单个树图像转换为广告牌云。我们的算法允许从最小的输入中自动生成任意数量的树木变化，因此提供了一种快速解决方案来增加户外场景中的植被多样性。