ReSplat: 学习递归高斯点散射
ReSplat: Learning Recurrent Gaussian Splatting
AI总结：
ReSplat通过递归高斯点散射方法，利用渲染误差反馈信号实现高效3D重建，减少高斯点数量并提升渲染速度，取得最佳性能。
摘要：
尽管现有的前馈高斯点散射模型在计算效率和稀疏视角设置下的泛化能力方面表现良好，但其性能受到依赖单次前向传递进行推理的根本限制。我们提出了ReSplat，一种前馈递归高斯点散射模型，通过迭代细化3D高斯点而无需显式计算梯度。我们的关键见解是，高斯点散射的渲染误差作为一种丰富的反馈信号，引导递归网络学习有效的高斯更新。这种反馈信号在测试时能自然适应未见的数据分布，使模型在不同数据集、视角数量和图像分辨率上实现稳健的泛化。为了初始化递归过程，我们引入了一个紧凑的重建模型，它在16倍下采样的空间中运行，产生比先前每像素高斯模型少16倍的高斯点。这大大减少了计算开销，并允许高效地进行高斯更新。在不同输入视角数量（2、8、16、32）、分辨率（256×256到540×960）和数据集（DL3DV、RealEstate10K和ACID）上的广泛实验表明，我们的方法在减少高斯点数量和提高渲染速度方面取得了显著成效，同时实现了最先进的性能。我们的项目页面位于https://haofeixu.github.io/resplat/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
