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Found 5 papers, 0 papers with code
Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
For ground material segmentation, we utilized an existing convolutional neural network architecture (i. e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data.
3D photogrammetry point cloud segmentation using a model ensembling framework
In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i. e., ground, human-made objects, and vegetation).
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
This paper discusses the next steps in extending our designed data segmentation framework for segmenting 3D city data.
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
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 locations and ground materials (Chen et al., 2019).
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
The results showed that 3D mesh trees could be replaced with geo-typical 3D tree models using the extracted individual tree locations.
