Plant Sam Gaussian Reconstruction (PSGR): A High-Precision and Accelerated Strategy for Plant 3D Reconstruction

Abstract

Plant 3D reconstruction plays a critical role in precision agriculture and plant growth monitoring, yet it faces challenges such as complex background interference, difficulties in capturing intricate plant structures, and a slow reconstruction speed. In this study, we propose PlantSamGaussianReconstruction (PSGR), a novel method that integrates Grounding SAM with 3D Gaussian Splatting (3DGS) techniques. PSGR employs Grounding DINO and SAM for accurate plant–background segmentation, utilizes algorithms such as Scale-Invariant Feature Transform (SIFT) for camera pose estimation and sparse point cloud generation, and leverages 3DGS for plant reconstruction. Furthermore, a 3D–2D projection-guided optimization strategy is introduced to enhance segmentation precision. The experimental results of various multi-view plant image datasets demonstrate that PSGR effectively removes background noise under diverse environments, accurately captures plant details, and achieves peak signal-to-noise ratio (PSNR) values exceeding 30 in most scenarios, outperforming the original 3DGS approach. Moreover, PSGR reduces training time by up to 26.9%, significantly improving reconstruction efficiency. These results suggest that PSGR is an efficient, scalable, and high-precision solution for plant modeling.

Plant phenotyping relevance

植物のマルチビュー画像から3D構造を再構成する手法を開発・評価しており、植物形態の取得が研究の中心です。

Code and data availability

The paper describes multi-view plant image datasets (three ornamental species) and a PSGR pipeline (Grounding DINO + SAM + 3DGS), but no public dataset, code, model, or supplement is provided. The Data Availability Statement says only 'Data are contained within the article,' and no repository or author URL for assets (

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