Process, challenges and solutions of fruit 3D reconstruction: A review

Abstract

Existing tasks such as fruit growth monitoring, harvesting, and quality sorting still suffer from low precision and insufficient automation. 3D reconstruction technology can accurately capture the external characteristics of fruits and shows great potential for enhancing automated fruit detection and processing. This paper, guided by two core application needs in the fruit industry: real-time online sensing and offline high-precision analysis, provides a detailed overview of research progress in 3D reconstruction technology for fruits. It first introduces the principles, workflows, and advantages and limitations of classical 3D reconstruction methods. Then, it focuses on the basic framework of learning-based 3D reconstruction approaches, their improvement directions, and their applications in fruits and other agricultural products. In addition, the challenges encountered in fruit 3D reconstruction, such as occlusion and complex lighting conditions, are summarized, along with potential solutions. Finally, future research directions are discussed. This review serves as a valuable reference for promoting the integration of computer vision and agricultural intelligence and advancing the fruit industry chain’s digital and intelligent transformation.

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.