Publication
Precise assessment of geometric building envelope characteristics is essential for parametric simulation analysis and informed retrofitting decisions. Previous methods for estimating building characteristics, such as window-to-wall ratio and building footprint area, primarily focus on planar properties from single images, limiting the accuracy and comprehensiveness required for complete 3D building envelope analysis. To address this limitation, we introduce BuildNet3D, a novel framework that leverages advanced neural surface reconstruction techniques based on signed distance function (SDF) representations for estimating geometric building characteristics. BuildNet3D integrates SDF representations with semantic modalities to recover fine-grained 3D geometry and semantics of building envelopes directly from 2D image inputs. Evaluations on complex synthetic and real-world building structures demonstrate its superior geometry reconstruction performance and higher accuracy in estimating window-to-wall ratios and building footprints compared to 2D methods. These results underscore the effectiveness of incorporating 3D representations to advance building envelope modeling, characteristic prediction, and practical applications in building analysis and retrofitting.