Results on MipNeRF360 and LLFF datasets. Side-by-side comparison of LGDWT-GS (ours) vs 3DGS demonstrating improved detail preservation and sharper reconstructions with our method.
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.
We release an open-source benchmarking package that defines standardized few-shot reconstruction protocols for evaluating 3DGS-based methods. The package provides comprehensive evaluation tools and metrics for sparse-view 3D reconstruction tasks.
We introduce a new multispectral greenhouse dataset containing four spectral bands. The dataset includes multiple plant species (sorghum, tomato, alocasia, cotton, grape) captured under controlled greenhouse conditions.
@article{salehi2024lgdwtgs,
title={LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction},
author={Salehi, Shima and Agashe, Atharva and McFarland, Andrew J. and Peeples, Joshua},
journal={arXiv preprint arXiv:2601.17185},
year={2026}
}
This material is based upon work supported by the Texas A&M University System Nuclear Security Office. Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing.