LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction

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Authors: Shima Salehi, Atharva Agashe, Andrew J. McFarland, Joshua Peeples
Cited by: 0

Abstract

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

Fields of Study & Subjects

3D Shape Modeling and Analysis Advanced Vision and Imaging 3D Surveying and Cultural Heritage

Publication details

Journal / source
arXiv (Cornell University)
Publication date
23 January 2026
Document type
Preprint