DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View

Abstract

Digital twin applications offered transformative potential by enabling real-time monitoring and robotic simulation through accurate virtual replicas of physical assets. The key to these systems is 3D reconstruction with high geometrical fidelity. However, existing methods struggled under field conditions, especially with sparse and occluded views. This study developed a two-stage framework (DATR) for the reconstruction of apple trees from sparse views. The first stage leverages onboard sensors and foundation models to semi-automatically generate tree masks from complex field images. Tree masks are used to filter out background information in multi-modal data for the single-image-to-3D reconstruction at the second stage. This stage consists of a diffusion model and a large reconstruction model for respective multi view and implicit neural field generation. The training of the diffusion model and LRM was achieved by using realistic synthetic apple trees generated by a Real2Sim data generator. The framework was evaluated on both field and synthetic datasets. The field dataset includes six apple trees with field-measured ground truth, while the synthetic dataset featured structurally diverse trees. Evaluation results showed that our DATR framework outperformed existing 3D reconstruction methods across both datasets and achieved domain-trait estimation comparable to industrial-grade stationary laser scanners while improving the throughput by $\sim$360 times, demonstrating strong potential for scalable agricultural digital twin systems.

Plant phenotyping relevance

リンゴ樹の疎視点画像から3D形状を再構成し、樹体形質を推定する手法の開発と、圃場・合成データでの評価が研究の中心であるため。

Code and data availability

The supplied blocks describe the DATR framework, field/synthetic datasets, and evaluation results, but contain no public availability statement, repository link, or deposit language for the paper's datasets, images, code, or trained models. The only URLs present (NPEC digital twin tomato project and farm-ng Amiga) are,

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