Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching
Elias Marks, Federico Magistri, Cyrill Stachniss
Abstract
Monitoring individual plants and computing precise 3D reconstructions is highly relevant for crop breeding. In the conventional breeding approach, humans measure phenotypic traits by hand, requiring substantial manual labor. This paper addresses precise 3D plant reconstructions in a crop field or breeding plot based on UAV imagery. We explicitly address the challenges resulting from the thin structures of leaves and naturally occurring self-occlusions. We combine photogrammetric bundle adjustment with a template-based matching approach and produce accurate 3D models that allow us to derive common, geometric traits used by breeders to phenotype plants. We provide a thorough experimental evaluation on commercially used sugar beet breeding plots to illustrate the capabilities of our method as well as its real world applicability.