Iterative Motion Compensation for Canonical 3D Reconstruction from UAV Plant Images Captured in Windy Conditions

Abstract

3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.

Plant phenotyping relevance

UAV画像から植物個体の高解像度3D形状を再構成する手法と、風による葉の動きを補正する技術を開発しており、植物表現型取得が中心です。データセット提供も含みます。

Code and data availability

The paper promises future release of code and a plant image dataset ('We will publicly release the source code... Additionally, we provide a dataset'), but no public URL, repository, or identifier is given in the supplied blocks. The only URL present (https://www.blender.org/) refers to a cited generic tool, not a this

No evidence-backed public reproduction asset is currently recorded.