Research on 3D reconstruction technology of fruit trees based on incremental analytical motion recovery technology

The precise and efficient reconstruction of three-dimensional structures stands as a pivotal cornerstone for the automated management of fruit trees. This study introduces a three-dimensional reconstruction methodology for fruit trees, leveraging incremental analytical scene motion recovery technology, alongside a tailored data acquisition framework for characteristic fruit trees in Xinjiang. By harnessing visual sensors, envelope image data of fruit trees is captured to form a panoramic multi-view image dataset. Key frames are extracted to derive a multi-view representation of the fruit tree, and incremental analytical scene motion recovery technology is employed to train pose information and generate a point cloud model, yielding a high-precision three-dimensional representation of the fruit tree. During experimentation, error analysis focused on the five key diameters of the target fruit trees, revealing reconstruction deviations ranging from 2.43mm to 5.24mm, with reconstruction errors falling between 4.31% and 5.93%. These findings underscore the methodology's capacity to strike a delicate balance between reconstruction precision and cost-effectiveness, offering enhanced technical underpinnings for the automated management of orchards.