用程序化方法从稀疏点云重建结构化3D建筑模型
ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points
- 设计领域语言将建筑结构表示为可转成网格的程序
- 通过编码器-解码器学习点云到程序的映射，生成高质量抽象
- 支持多视图图像和自然语言输入，适合建筑数字化场景
我们提出ArcPro，一种基于架构程序的新学习框架，用于从高度稀疏且低质量的点云中恢复结构化的3D抽象。具体而言，我们设计了一种领域特定语言（DSL），以层次化方式将建筑结构表示为程序，可高效转换为网格。通过前向过程合成训练数据，实现前向与逆向过程建模的桥梁，使网络能进行反向预测。在点云-程序对上训练编码器-解码器，建立从非结构化点云到架构程序的映射；其中3D卷积编码器提取点云特征，变换器解码器自回归地以分词形式预测程序。推理过程高效，生成结果合理且忠实。大量实验表明，ArcPro优于传统建筑代理重建及基于学习的抽象方法。我们还探索了其与多视图图像和自然语言输入的结合潜力。
原文摘要 · Abstract (English)
We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically, we design a domain-specific language (DSL) to hierarchically represent building structures as a program, which can be efficiently converted into a mesh. We bridge feedforward and inverse procedural modeling by using a feedforward process for training data synthesis, allowing the network to make reverse predictions. We train an encoder-decoder on the points-program pairs to establish a mapping from unstructured point clouds to architectural programs, where a 3D convolutional encoder extracts point cloud features and a transformer decoder autoregressively predicts the programs in a tokenized form. Inference by our method is highly efficient and produces plausible and faithful 3D abstractions. Comprehensive experiments demonstrate that ArcPro outperforms both traditional architectural proxy reconstruction and learning-based abstraction methods. We further explore its potential to work with multi-view image and natural language inputs.
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