Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry

We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way.


3D Plant Modeling: Localization, Mapping and Segmentation for Plant Phenotyping Using a Single Hand-held Camera

In this work, structure from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants’ surfaces. Multiple-view stereo is employed to extend the sparse model to a dense 3D point cloud. The model is automatically segmented by spectral clustering, properly separating the plant’s leaves whose surfaces are estimated by fitting trimmed B-splines to their 3D points.