Lightweight contour-aware 2D Gaussian splatting under Plant-to-Camera

Abstract

The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping

Plant phenotyping relevance

植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a self-collected maize/wheat Plant-to-Camera dataset (143 specimens) and a custom 2DGS pipeline, but contain no authors' public code, data, or model deposit with an actionable URL. The only public dataset mentioned (the publicly available wheat dataset [24]) is cited prior work, not a paper

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