A-Scan2BIM: Assistive Scan to Building Information Modeling
Weilian Song Jieliang Luo Dale Zhao Yan Fu Chin-Yi Cheng Yasutaka Furukawa
This paper proposes an assistive system for architects that converts a
large-scale point cloud into a standardized digital representation of a
building for Building Information Modeling (BIM) applications. The process is
known as Scan-to-BIM, which requires many hours of manual work even for a
single building floor by a professional architect. Given its challenging
nature, the paper focuses on helping architects on the Scan-to-BIM process,
instead of replacing them. Concretely, we propose an assistive Scan-to-BIM
system that takes the raw sensor data and edit history (including the current
BIM model), then auto-regressively predicts a sequence of model editing
operations as APIs of a professional BIM software (i.e., Autodesk Revit). The
paper also presents the first building-scale Scan2BIM dataset that contains a
sequence of model editing operations as the APIs of Autodesk Revit. The dataset
contains 89 hours of Scan2BIM modeling processes by professional architects
over 16 scenes, spanning over 35,000 m^2. We report our system's reconstruction
quality with standard metrics, and we introduce a novel metric that measures
how natural the order of reconstructed operations is. A simple modification to
the reconstruction module helps improve performance, and our method is far
superior to two other baselines in the order metric. We will release data,
code, and models at a-scan2bim.github.io.