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LEAF: Learning Edge Attraction Filed for Precise Roof Wireframe Reconstruction

Qiaoqiao Hao,Zhaoliang Liu,Duxin Zhu,Jinhe Su,Zheng Gong,Yundong Wu,Guorong Cai

Published 2024 in EITCE

ABSTRACT

Significant progress has been made in roof wireframe reconstruction. However, previous work faced a major issue of positive-negative sample imbalance in edge detection using fully connected graphs of corner points. This led to incomplete roof structures and redundant edges. In this paper, we introduce a new learning point for edge attraction fields, called LEAF, for roof wireframe reconstruction. We observed that points distributed along the edges determine whether there is a connection between diagonal points. To address this, we designed an edge point detector that screens edge points based on their spatial relationship to the edge. Additionally, we developed an edge attraction field extractor to compute the direction vector of the nearest edge for these points. This allows us to use the main directional tendencies of points around corner lines to verify true connections between corner points. By selecting diagonal connections, our method reduces false connections in the fully connected graph and addresses the sample imbalance, resulting in a complete roof wireframe. In experiments on synthetic datasets Point2Roof and the Real-world Building3D dataset, our method achieved superior performance. Notably, we improved Edge recall by 20 percentage points on Building3D. Our approach enhances all edge parameters on both datasets, outperforming existing wireframe reconstruction methods.

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