Lei Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lei Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Baolong Li
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
1 shared publication - Chenrong Fang
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
1 shared publication - Kan Chen
Centre for Immersification, Singapore Institute of Technology, Singapore, Singapore
1 shared publication - Qian Sun
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
1 shared publication - Wei Li
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
1 shared publication
Latest Publications
- Open Access
- 8 April 2026
- Published by MDPI AG in Journal of Imaging
- Vol. 12 (4) , 161
RegionGraph: Region-Aware Graph-Based Building Reconstruction from Satellite Imagery
Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure.
- 8 April 2026
- Published by MDPI AG in Journal of Imaging
- Vol. 12 (4) , 161
Open AccessRegionGraph: Region-Aware Graph-Based Building Reconstruction from Satellite Imagery
Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure.