Single-view-based high-fidelity three-dimensional reconstruction of leaves

Abstract

In modern agricultural science research, high-fidelity three-dimensional (3D) leaf models are crucial for crop growth analysis. However, reconstructing the complex morphology and texture of leaves from a single viewpoint under varying natural lighting conditions poses a significant challenge. To address the issues associated with this challenge, this paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject. In the camera prediction process, the combination of an explicit point cloud generation technique and an implicit 3D Gaussian rendering technique enables the accurate prediction of camera parameters and the effective capture of leaf phenotypic features. In the synthesis of the 3D model of the leaf, a strategy for optimizing the coarse model UV texture is designed with the objective of achieving spatial consistency of texture details. Furthermore, the model was successfully applied to the reconstruction of other crop leaves and lamellar structural objects, and innovatively constructed a leaf reconstruction model with disease characteristics, aiming to provide a reference for the early 3D detection of crop diseases, as well as a reference for the 3D reconstruction and visualization of other lamellar objects. The results demonstrate that the method is effective in reconstructing the morphological structure and texture details of leaves, as well as thin sheet-like structured objects, achieving fast and high-fidelity single-view reconstruction.

Plant phenotyping relevance

単一画像から葉の形態・テクスチャを高精度に3D再構成する手法を開発しており、葉の表現型特徴抽出が中心的な技術貢献である。

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