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.
This paper shows as the state of the art in structure from motion and multiple view stereo is able to produce accurate 3D models for specimens presenting sparse canopies. Three-dimensional triangular meshes are computed from a set of non-calibrated images, modeling a basil and an Ixora specimens and accurately representing their leaves and branches.