Edge First: Edge-Guided Geometry for Superior 3D Roof Wireframe Reconstruction

Qiaoqiao Hao, Ting Han, Yujun Liu, Shangfeng Huang, Duxin Zhu, Jinhe Su, Yundong Wu, Guorong Cai

Abstract: Roof wireframe reconstruction has shown great success in 3D building reconstruction due to its lightweight nature and straightforward representation. However, previous methods consider all roof points, which result in edge redundancy and omissions. In this paper, we propose a novel and streamlined Edge-guided Geometric wireframe reconstruction framework, named EDGE. We find that points distributed along the roof edges make a significant contribution to the precise geometric structure of wireframe. Therefore, we design an edge point extractor (EPE) to capture the spatial relationship between points and edges, filtering out internal plane points. Moreover, we discover that the previous edge detectors rely solely on corner points, leading to error accumulation. To address this, we present the Hybrid Edge Detector (HED) feeding corner points with edge contextual features, which not only enhances edge completeness but also mitigates edge redundancy. Comprehensive experiments demonstrate EDGE outperforms existing wireframe reconstruction methods with 0.86 Corner F1-score and 0.71 Edge F1-score on Building3D dataset, striking the significant improvement of accuracy between corner and edge. Notably, our EDGE achieves a significant improvement of over 11% in Edge Recall, demonstrating the effectiveness and robustness of the proposed method.

External IDs:dblp:conf/icassp/Hao00HZSWC25