用小波域扩散加速稀疏视角3D高斯重建，训练更快更省力。
WaveletGaussian: Wavelet-domain Diffusion for Sparse-view 3D Gaussian Object Reconstruction
- 将扩散模型移到小波低频子带，高频部分用轻量网络修复
- 在两个基准数据集上达到接近最优的渲染质量
- 在线随机掩码策略替代旧方法，训练效率显著提升
3D高斯点云（3DGS）已成为基于图像物体重建的强大表示，但在稀疏视角设置下性能急剧下降。现有方法通过扩散模型修复损坏的渲染结果，再将其作为伪真值用于后续优化，虽有效但计算开销巨大。本文提出WaveletGaussian，一种更高效的稀疏视角3D高斯物体重建框架。核心思想是将扩散过程转移到小波域：仅对低分辨率LL子带应用扩散，而高频子带则由轻量级网络进行细化。此外，我们提出一种高效的在线随机掩码策略，用于构建扩散微调的训练样本对，取代常用但低效的留一法策略。在Mip-NeRF 360和OmniObject3D两个基准数据集上的实验表明，WaveletGaussian在显著降低训练时间的同时，仍能实现具有竞争力的渲染质量。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。