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3D Reconstruction of Small Flexible Objects With Slender Structures: Reconstructing Conifer Seedlings for Development of Computer Vision Systems in Virtual Environments

The Photogrammetric Record · 1 Apr 2026 · 10.1111/phor.70054

Abstract

ABSTRACT 3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine‐scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1‐score, mask intersection‐over‐union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1‐score of 86.1%. Pine seedlings yielded higher recall and F1‐scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.

Plant phenotyping relevance

針葉樹苗の形状・テクスチャを取得する3D画像再構成ワークフローを開発・比較・定量評価しており、植物フェノタイピング手法が中心である。

abstractThis study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments.
abstractThe proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.

Code and data availability

The paper's Data Availability Statement states that the raw seedling image data and finalized textured meshes (the paper's phenotyping/3D reconstruction inputs and outputs) are freely available on Zenodo under DOI 10.5281/zenodo.19823955, which appears in the allowed URL list.

Datasetpublic

and without adjusting the scanning parameters, while also re- Data Availability Statement taining texture and color. In contrast to prior approaches that require manual intervention or do not preserve visual informa- Raw image data and finalized textured meshes are freely available at Zenodo.​org with https://​doi.​org/​10.​5281/​zenodo.​19823955. tion, the proposed workflow enables a semi-­automated recon- struction process suitable for dataset generation. As shown, the methodology is effective for the digital reconstruction of small References and slender structured flexible objects and holds potential for Abbood, S. A., H. A. Ajjah, A. H. H. Alboabidallah, M. U. Moha

Open resource ↗Zenodo · pdf-layout-page:14 lines:50-74

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