High-Resolution Plant Shape Measurements from Multi-View Stereo Reconstruction
Abstract
Accurate high-resolution 3D models are essential for a non-invasive analysis of phenotypic characteristics of plants. Leaf surface areas, fruit volumes and leaf inclination angles are typically of interest. This work presents a globally optimal 3D geometry reconstruction method that is specialized to high-resolutions and is thus suitable to reconstruct thin structures typically occuring in the geometry of plants. Volumetric 3D models are computed in a convex optimization framework from a set of RGB input images depicting the plant from different view points. The method uses the memory and run-time efficient octree data structure for fast computations of high-resolution 3D models. Results show accurate 3D reconstructions of barley, while an increase in resolution of a factor of up to 2000 is achieved in comparison to the use of a uniform voxel based data structure, making the choice of data structure crucial for feasible resolutions.
Cite
Text
Klodt and Cremers. "High-Resolution Plant Shape Measurements from Multi-View Stereo Reconstruction." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-16220-1_13Markdown
[Klodt and Cremers. "High-Resolution Plant Shape Measurements from Multi-View Stereo Reconstruction." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/klodt2014eccv-high/) doi:10.1007/978-3-319-16220-1_13BibTeX
@inproceedings{klodt2014eccv-high,
title = {{High-Resolution Plant Shape Measurements from Multi-View Stereo Reconstruction}},
author = {Klodt, Maria and Cremers, Daniel},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {174-184},
doi = {10.1007/978-3-319-16220-1_13},
url = {https://mlanthology.org/eccv/2014/klodt2014eccv-high/}
}