4 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CV2020★ 3 cited
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
Meida Chen, Andrew Feng, Kyle McCullough +3
With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing Maps team has created over 125 highly detailed 3D cities from 11 different countries that cover hundred…
cs.CV2020★ 4 cited
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
Meida Chen, Andrew Feng, Kyle McCullough +3
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locati…
cs.CV2020★ 2 cited
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
Meida Chen, Andrew Feng, Kyle McCullough +4
Our previous works have demonstrated that visually realistic 3D meshes can be automatically reconstructed with low-cost, off-the-shelf unmanned aerial systems (UAS) equipped with c…