Segment Any Architectural Facades (SAAF)：一种基于多模态语义引导的建筑立面、墙体与窗户自动分割模型
Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance
AI总结：
针对建筑立面墙体与窗户分割，提出基于多模态语义引导的SAAF模型，融合文本与图像特征，端到端训练，在多个数据集上mIoU优于现有方法，提升精度与泛化能力。
摘要：
在建筑数字化发展的背景下，墙体与窗户的自动分割是提升建筑信息模型和计算机辅助设计效率的关键步骤。本研究提出了一种基于多模态语义引导的建筑立面墙体与窗户自动分割模型，称为Segment Any Architectural Facades (SAAF)。首先，SAAF具有多模态语义协同特征提取机制。通过结合自然语言处理技术，它能够将文本描述中的语义信息与图像特征相融合，增强对建筑立面构件的语义理解。其次，我们开发了一个端到端的训练框架，使模型能够自主学习从文本描述到图像分割的映射关系，减少人工干预对分割结果的影响，提高模型的自动化程度和鲁棒性。最后，我们在多个立面数据集上进行了大量实验。SAAF的分割结果在mIoU指标上优于现有方法，表明SAAF模型在面对多样化数据集时能够保持高精度的分割能力。我们的模型在提高墙体与窗户分割任务的准确性和泛化能力方面取得了一定进展。预计它将为建筑计算机视觉技术的发展提供参考，并为多模态学习在建筑领域的应用探索新的思路和技术路径。
英文摘要：
In the context of the digital development of architecture, the automatic segmentation of walls and windows is a key step in improving the efficiency of building information models and computer-aided design. This study proposes an automatic segmentation model for building facade walls and windows based on multimodal semantic guidance, called Segment Any Architectural Facades (SAAF). First, SAAF has a multimodal semantic collaborative feature extraction mechanism. By combining natural language processing technology, it can fuse the semantic information in text descriptions with image features, enhancing the semantic understanding of building facade components. Second, we developed an end-to-end training framework that enables the model to autonomously learn the mapping relationship from text descriptions to image segmentation, reducing the influence of manual intervention on the segmentation results and improving the automation and robustness of the model. Finally, we conducted extensive experiments on multiple facade datasets. The segmentation results of SAAF outperformed existing methods in the mIoU metric, indicating that the SAAF model can maintain high-precision segmentation ability when faced with diverse datasets. Our model has made certain progress in improving the accuracy and generalization ability of the wall and window segmentation task. It is expected to provide a reference for the development of architectural computer vision technology and also explore new ideas and technical paths for the application of multimodal learning in the architectural field.
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