Compression techniques for 3D Gaussian Splatting (3DGS) have recently achieved considerable success in minimizing storage overhead for 3D Gaussians while preserving high rendering quality. Despite the impressive storage reduction, the lack of learned priors restricts further advances in the rate-distortion trade-off for 3DGS compression tasks. To address this, we introduce a novel 3DGS compression framework that leverages the powerful representational capacity of learned image priors to recover compression-induced quality degradation. Built upon initially compressed Gaussians, our restoration network effectively models the compression artifacts in the image space between degraded and original Gaussians. To enhance the rate-distortion performance, we provide coarse rendering residuals into the restoration network as side information. By leveraging the supervision of restored images, the compressed Gaussians are refined, resulting in a highly compact representation with enhanced rendering performance. Our framework is designed to be compatible with existing Gaussian compression methods, making it broadly applicable across different baselines. Extensive experiments validate the effectiveness of our framework, demonstrating superior rate-distortion performance and outperforming the rendering quality of state-of-the-art 3DGS compression methods while requiring substantially less storage.

翻译：针对三维高斯泼溅（3DGS）的压缩技术近期在显著降低三维高斯模型存储开销的同时，成功保持了高质量的渲染效果。尽管存储压缩已取得显著进展，但缺乏学习先验的限制阻碍了3DGS压缩任务在率失真权衡方面的进一步突破。为此，我们提出了一种新颖的3DGS压缩框架，该框架利用学习图像先验的强大表示能力来恢复由压缩引起的质量退化。我们的复原网络基于初始压缩后的高斯模型，有效地在图像空间中建模了退化高斯模型与原始高斯模型之间的压缩伪影。为了提升率失真性能，我们将粗略的渲染残差作为边信息输入复原网络。通过利用复原图像的监督，压缩后的高斯模型得以优化，从而生成具有增强渲染性能的高度紧凑表示。本框架设计为与现有高斯压缩方法兼容，使其能够广泛适用于不同的基线方法。大量实验验证了我们框架的有效性，其在率失真性能上表现优异，渲染质量超越了当前最先进的3DGS压缩方法，同时所需存储空间大幅减少。