通过迭代优化提升3D高斯点渲染效果，无需反向传播。
ReSplat: Learning Recurrent Gaussian Splatting
- 用渲染误差作为反馈信号，递归更新3D高斯分布。
- 在2~32视角下均超越现有方法，高斯数量减少16倍。
- 适合追求高效、低资源3D重建的开发者使用。
现有前馈式高斯点渲染模型虽计算高效且能泛化至稀疏视角，但其性能受限于单次前向传播。本文提出ReSplat，一种前馈递归高斯点渲染模型，通过迭代方式优化3D高斯分布，无需显式计算梯度。核心思想是利用高斯点渲染误差作为丰富反馈信号，引导递归网络学习有效高斯更新策略。该信号可自适应未见数据分布，实现跨数据集、视角数量与图像分辨率的鲁棒泛化。为初始化递归过程，引入一个紧凑重构模型，在16×下采样空间中运行，生成的高斯数量仅为以往逐像素模型的1/16，显著降低计算开销，支持高效更新。在不同输入视角（2、8、16、32）、分辨率（256×256至540×960）及数据集（DL3DV、RealEstate10K、ACID）上的广泛实验表明，本方法在保持状态领先性能的同时，大幅减少高斯数量并提升渲染速度。
原文摘要 · Abstract (English)
While existing feed-forward Gaussian splatting models offer computational efficiency and can generalize to sparse view settings, their performance is fundamentally constrained by relying on a single forward pass for inference. We propose ReSplat, a feed-forward recurrent Gaussian splatting model that iteratively refines 3D Gaussians without explicitly computing gradients. Our key insight is that the Gaussian splatting rendering error serves as a rich feedback signal, guiding the recurrent network to learn effective Gaussian updates. This feedback signal naturally adapts to unseen data distributions at test time, enabling robust generalization across datasets, view counts, and image resolutions. To initialize the recurrent process, we introduce a compact reconstruction model that operates in a $16 \times$ subsampled space, producing $16 \times$ fewer Gaussians than previous per-pixel Gaussian models. This substantially reduces computational overhead and allows for efficient Gaussian updates. Extensive experiments across varying number of input views (2, 8, 16, 32), resolutions ($256 \times 256$ to $540 \times 960$), and datasets (DL3DV, RealEstate10K, and ACID) demonstrate that our method achieves state-of-the-art performance while significantly reducing the number of Gaussians and improving the rendering speed. Our project page is at https://haofeixu.github.io/resplat/.
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