3D Gaussian Splatting (3DGS) has become a powerful representation for image-based object reconstruction, yet its performance drops sharply in sparse-view settings. Prior works address this limitation by employing diffusion models to repair corrupted renders, subsequently using them as pseudo ground truths for later optimization. While effective, such approaches incur heavy computation from the diffusion fine-tuning and repair steps. We present WaveletGaussian, a framework for more efficient sparse-view 3D Gaussian object reconstruction. Our key idea is to shift diffusion into the wavelet domain: diffusion is applied only to the low-resolution LL subband, while high-frequency subbands are refined with a lightweight network. We further propose an efficient online random masking strategy to curate training pairs for diffusion fine-tuning, replacing the commonly used, but inefficient, leave-one-out strategy. Experiments across two benchmark datasets, Mip-NeRF 360 and OmniObject3D, show WaveletGaussian achieves competitive rendering quality while substantially reducing training time.

翻译：三维高斯泼溅（3DGS）已成为基于图像的对象重建的一种强大表示方法，但其在稀疏视角设置下的性能会急剧下降。先前的研究通过采用扩散模型来修复受损的渲染图像，随后将其作为伪真值用于后续优化，以应对这一局限。虽然有效，但此类方法因扩散微调和修复步骤而产生了沉重的计算负担。我们提出了WaveletGaussian，一个用于更高效稀疏视角三维高斯对象重建的框架。我们的核心思想是将扩散过程移至小波域：扩散仅应用于低分辨率的LL子带，而高频子带则通过一个轻量级网络进行细化。我们进一步提出了一种高效的在线随机掩码策略，用于为扩散微调构建训练对，取代了常用但低效的留一策略。在两个基准数据集Mip-NeRF 360和OmniObject3D上的实验表明，WaveletGaussian在显著减少训练时间的同时，实现了具有竞争力的渲染质量。