Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors

作     者：Lee, Jae Joong Li, Bosheng Beery, Sara Huang, Jonathan Fei, Songlin Yeh, Raymond A. Benes, Bedrich 

作者机构：Purdue University Department of Computer Science United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States Google United States Purdue University Department of Forestry Natural Resources United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Diffusion 

摘      要：We introduce Tree-D Fusion, featuring the first collection of 600,000 environmentally aware, 3D simulation-ready tree models generated through Diffusion priors. Each reconstructed 3D tree model corresponds to an image from Google s Auto Arborist Dataset, comprising street view images and associated genus labels of trees across North America. Our method distills the scores of two tree-adapted diffusion models by utilizing text prompts to specify a tree genus, thus facilitating shape reconstruction. This process involves reconstructing a 3D tree envelope filled with point markers, which are subsequently utilized to estimate the tree s branching structure using the space colonization algorithm conditioned on a specified genus. © 2024, CC BY.