42 citations · 52 across the 6 of their papers we have counts for
6 papers
A New Method of Pixel-level In-situ U-value Measurement for Building Envelopes Based on Infrared Thermography
Zihao Wang, Yu Hou, Lucio Soibelman
The potential energy loss of aging buildings traps building owners in a cycle of underfunding operations and overpaying maintenance costs. Energy auditors intending to generate an…
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Meida Chen, Qingyong Hu, Zifan Yu +6
Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale d…
Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
Meida Chen, Andrew Feng, Yu Hou +3
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned…
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…