We propose a new method for few-shot 3D reconstruction that integrates global and local frequency regularization to stabilize geometry and preserve fine details under sparse-view conditions, addressing a key limitation of existing 3D Gaussian Splatting (3DGS) models. We also introduce a new multispectral greenhouse dataset containing four spectral bands captured from diverse plant species under controlled conditions. Alongside the dataset, we release an open-source benchmarking package that defines standardized few-shot reconstruction protocols for evaluating 3DGS-based methods. Experiments on our multispectral dataset, as well as standard benchmarks, demonstrate that the proposed method achieves sharper, more stable, and spectrally consistent reconstructions than existing baselines. The dataset and code for this work are publicly available

翻译：我们提出了一种面向少样本三维重建的新方法，通过融合全局与局部频率正则化，在稀疏视图条件下稳定几何结构并保留精细细节，从而解决现有三维高斯溅射（3DGS）模型的关键局限性。同时，我们引入了一个新型多光谱温室数据集，包含受控条件下多种植物物种的四个光谱波段。除数据集外，我们发布了一个开源基准测试工具包，定义了标准化少样本重建协议，用于评估基于3DGS的方法。在多光谱数据集及标准基准上的实验表明，与现有基线方法相比，所提方法能够实现更清晰、更稳定且光谱一致的场景重建。本工作的数据集与代码均已公开。