用Transformer从点云反推建筑程序化抽象描述。
Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers
- 通过Transformer学习点云到程序化建筑描述的逆映射。
- 重建几何与结构准确，修复缺失部分保持结构一致。
- 适合游戏/动画领域需要高效渲染的建筑生成任务。
我们通过学习反转程序化建筑模型，生成反映其几何与结构本质的建筑抽象。首先构建了配对的抽象程序化建筑模型与模拟点云数据集，随后利用Transformer学习逆映射。给定一个点云，训练后的Transformer可推断出对应的程序化语言描述的抽象建筑。该方法借助游戏与动画领域开发的表达性强的程序化模型，从而保留高效渲染和规则性、对称性等先验优势。实验表明，该方法在几何与结构重建上表现良好，并能实现结构一致的补全。
原文摘要 · Abstract (English)
We generate abstractions of buildings, reflecting the essential aspects of their geometry and structure, by learning to invert procedural models. We first build a dataset of abstract procedural building models paired with simulated point clouds and then learn the inverse mapping through a transformer. Given a point cloud, the trained transformer then infers the corresponding abstracted building in terms of a programmatic language description. This approach leverages expressive procedural models developed for gaming and animation, and thereby retains desirable properties such as efficient rendering of the inferred abstractions and strong priors for regularity and symmetry. Our approach achieves good reconstruction accuracy in terms of geometry and structure, as well as structurally consistent inpainting.
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