IMPLICITY: CITY MODELING FROM SATELLITE IMAGES WITH DEEP IMPLICIT OCCUPANCY FIELDS

8 citations as recorded by crossref.

1. Reconstructing Building Height From Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network Z. Chen et al. https://doi.org/10.1109/TGRS.2026.3656340
2. Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching W. Iqbal et al. https://doi.org/10.1007/s41064-023-00252-0
3. Points2Model: a neural-guided 3D building wireframe reconstruction from airborne LiDAR point clouds P. Akwensi et al. https://doi.org/10.1080/17538947.2025.2458682
4. Remote Sensing for Understanding the Vertical Dimension of Urban Landscapes: A review on data, products, methods, and prospects H. Qin et al. https://doi.org/10.1109/MGRS.2026.3684995
5. PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds Z. Chen et al. https://doi.org/10.1016/j.isprsjprs.2024.09.031
6. KIBS: 3D detection of planar roof sections from a single satellite image J. Lussange et al. https://doi.org/10.1016/j.isprsjprs.2024.11.014
7. Learning visual overlapping image pairs for SfM via CNN fine-tuning with photogrammetric geometry information Q. Hou et al. https://doi.org/10.1016/j.jag.2022.103162
8. Enhancing Photogrammetric DSM Based on Multiscale and Domain-Invariant Semantic Feature Learning X. Wang et al. https://doi.org/10.1109/JSTARS.2025.3628888