通过基于空间条件的预测增强3D高斯泼溅压缩
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction
AI总结：
本文提出一种基于空间条件预测的3DGS压缩框架，利用网格场景信息预测、残差补偿及实例感知超先验熵模型，在保持渲染质量的同时比SOTA节省24.42%比特率。
摘要：
最近，3D Gaussian Splatting (3DGS) 因其卓越的实时渲染性能而在新视角合成 (NVS) 中获得了广泛关注。然而，vanilla 3DGS 巨大的存储和传输成本（单个场景需要数百兆甚至数千兆字节）阻碍了其进一步应用。受视频压缩中预测技术的启发，我们将预测技术引入基于锚点的 Gaussian 表示中，以有效降低比特率。具体而言，我们提出了一个基于空间条件的预测模块，利用网格捕获的场景信息进行预测，并设计了残差补偿策略来学习缺失的细粒度信息。此外，为了进一步压缩残差，我们提出了一个实例感知的 hyper prior，开发了结构感知和实例感知的熵模型。大量实验证明了我们基于预测的压缩框架及每个技术组件的有效性。即使与 SOTA 压缩方法相比，我们的框架仍然实现了 24.42% 的比特率节省。代码即将发布！
英文摘要：
Recently, 3D Gaussian Spatting (3DGS) has gained widespread attention in Novel View Synthesis (NVS) due to the remarkable real-time rendering performance. However, the substantial cost of storage and transmission of vanilla 3DGS hinders its further application (hundreds of megabytes or even gigabytes for a single scene). Motivated by the achievements of prediction in video compression, we introduce the prediction technique into the anchor-based Gaussian representation to effectively reduce the bit rate. Specifically, we propose a spatial condition-based prediction module to utilize the grid-captured scene information for prediction, with a residual compensation strategy designed to learn the missing fine-grained information. Besides, to further compress the residual, we propose an instance-aware hyper prior, developing a structure-aware and instance-aware entropy model. Extensive experiments demonstrate the effectiveness of our prediction-based compression framework and each technical component. Even compared with SOTA compression method, our framework still achieves a bit rate savings of 24.42 percent. Code is to be released!
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。
