6 citations · 20 across the 8 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…
Learning Formation of Physically-Based Face Attributes
Ruilong Li, Karl Bladin, Yajie Zhao +8
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level res…