发表机构
Aalborg University; Pioneer Centre for Artificial Intelligence（奥尔堡大学; 人工智能先锋中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出BIMScript，通过扩展布局语言属性、优化解码方案、改进粒度处理，实现合成扫描数据到BIM工具的端到端自动导入，且支持大语言模型驱动的可持续性推理。
AI 中文摘要
SceneScript等结构化语言模型将场景重建为包含参数化命令的短程序，是一种本质上可编辑且语义明确的表示形式。本文研究了这类模型在现有建筑自动导入BIM工具这一极具吸引力的应用场景中存在的三个问题（基于合成扫描数据开展研究）：场景由什么构成、生成速度有多快、每个元素的确切位置是什么。BIMScript在单一语法框架内解决了这三个问题。首先，我们在布局语言中为每个元素扩展了「材料」和「状态」属性，这些属性由我们构建的包含10万个合成场景（190万个伪标记元素）的视觉语言模型材料通行证语料库进行监督，并通过提升特征点编码器将图像外观映射到材料令牌。其次，我们发现这些程序的自回归解码并非受计算限制，而是受内核启动和主机同步开销主导，因此我们采用输出精确的CUDA图解码器（每步耗时1.9毫秒，对比6.4毫秒，提速3.4倍），并结合利用确定性实体模式的语法并行、容差验证的草稿-验证方案来消除该开销。第三，我们通过无训练的几何对齐和回归子箱偏移的混合离散-连续解码器头，解决了模型5厘米令牌网格粒度的问题，并分别测量了两者对残差误差的恢复量。由于每个命令与原生Revit对象一一对应，我们通过工作加载项及其IFC4导出验证了直接导入BIM创作工具的端到端流程，且该程序的语言形式还设计为支持大语言模型驱动的、面向建筑资产的可持续性感知推理。
英文摘要
Structured-language models such as SceneScript reconstruct a scene as a short program of parametric commands, an inherently editable and semantically explicit representation. We ask three questions that stand between such models and their most compelling application, automated ingestion of existing buildings into BIM tools, studied here on synthetic scans: \emph{what} is the scene made of, \emph{how fast} can it be produced, and \emph{exactly where} is each element. BIMScript answers all three within one grammar. First, we extend the layout language with per-element \emph{material} and \emph{condition} attributes, supervised by a vision-language-model material-passport corpus we build over 100k synthetic scenes (1.9M pseudo-labeled elements), and route image appearance to the material tokens through a lifted-feature point encoder. Second, we show that autoregressive decoding of these programs is dominated not by compute but by kernel-launch and host-synchronization overhead, and remove it with an output-exact CUDA-graph decoder (1.9 vs 6.4\,ms/step, $3.4\times$) plus a grammar-parallel, tolerance-verified draft-and-verify scheme that exploits the deterministic entity schema. Third, we address the model's 5cm token-grid granularity with training-free geometric snapping and a hybrid discrete--continuous decoder head that regresses a sub-bin offset, and measure how much of the residual error each recovers. Because each command maps one-to-one onto a native Revit object, we validate direct ingestion into a BIM authoring tool end to end with a working add-in and its IFC4 export, and the same program's language form is designed to support LLM-driven, sustainability-aware reasoning over the built asset.
CommentsProject Page: see https://bimscriptworld.github.io/BIMScript/ ; to be published in ECCV 2026 TwinWorld Workshop
