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Automatic reconstruction of tree skeletal structures from point clouds

机译:从点云自动重建树木骨骼结构

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摘要

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 skeletal structures of trees, from which full geometry can be generated. The core of our method is a series of global optimizations that fit skeletal 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.
机译:树木、灌木和其他植物在城市环境中无处不在,逼真的树木模型可以为数字城市场景增添大量真实感。关于树结构建模的研究很多,但关于重建真实世界树木几何形状的工作有限——即便如此,大多数工作都集中在通过大量用户交互辅助的照片进行重建。在本文中,我们对真实世界的植被进行了主动激光扫描,并提出了一种自动方法,可以稳健地重建树木的骨骼结构,从中可以生成完整的几何形状。我们方法的核心是一系列全局优化,这些优化将骨架结构拟合到通常稀疏、不完整和嘈杂的点数据中。我们方法的一个显著优势是它能够同时重建多个重叠的树,而无需分割。我们在许多不同树种的原始扫描中证明了我们方法的有效性和稳健性。

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