Bird-View 3D Reconstruction for Crops with Repeated Textures
Guoyu Lu
Abstract
Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close-range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multi-view real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield.