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DATR: Diffusion-Based 3D Apple Tree Reconstruction Framework With Sparse-View
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DOI：10.1109/LRA.2026.3655283.png)
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摘要
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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 the DATR framework for apple tree reconstruction from sparse views. DATR automatically generates tree masks from field images using onboard sensors and foundation models, which filter background information from multi-modal data for reconstruction. Leveraging the multi-modality, a diffusion model generates novel views and a large reconstruction model produces implicit neural fields. Geometric priors enable scale retrieval to align output with real-world dimensions. Both models were trained 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. The evaluation results indicated that our DATR framework surpassed existing 3D reconstruction techniques on both datasets, providing a favorable balance of accuracy and efficiency. It achieved domain-trait estimation performance on par with industrial-grade stationary laser scanners, while increasing scan throughput by approximately 1000×, thereby underscoring its strong promise for scalable agricultural digital twin systems.
Keyword:
Digital twin
agricultural robotics
robot perception
few-shot 3D reconstruction
Real2Sim closed loop
期刊
I
IF:
5.3
论文数:
1.8K
被引数:
3.9W
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