LGDWT-GS: 基于局部和全局离散小波正则化的稀疏视图场景重建
LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction
AI总结：
LGDWT-GS通过整合局部和全局频率正则化，提升稀疏视图下3D重建的几何稳定性与细节保留能力，并提供多光谱数据集和开源基准测试工具。
摘要：
我们提出了一种新的方法，用于少样本3D重建，该方法整合了全局和局部频率正则化，以在稀疏视图条件下稳定几何并保留精细细节，解决现有3D高斯点散射（3DGS）模型的关键限制。我们还引入了一个新的多光谱温室数据集，包含四个光谱带，从不同植物物种在受控条件下捕获。除了数据集，我们还发布了一个开源基准测试包，定义了标准化的少样本重建协议，用于评估基于3DGS的方法。在我们的多光谱数据集以及标准基准上的实验表明，所提出的方法在重建效果上比现有基线更清晰、更稳定且光谱一致。数据集和代码已公开可用。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
