3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challenges for practical applications. In this paper, we introduce an efficient compression technique that significantly reduces storage overhead by using compact representation. We propose a unified architecture that combines point cloud data and feature planes through a progressive tri-plane structure. Our method utilizes 2D feature planes, enabling continuous spatial representation. To further optimize these representations, we incorporate entropy modeling in the frequency domain, specifically designed for standard video codecs. We also propose channel-wise bit allocation to achieve a better trade-off between bitrate consumption and feature plane representation. Consequently, our model effectively leverages spatial correlations within the feature planes to enhance rate-distortion performance using standard, non-differentiable video codecs. Experimental results demonstrate that our method outperforms existing methods in data compactness while maintaining high rendering quality.
3D 高斯点绘制（3D Gaussian Splatting）是一种备受认可的 3D 场景表示方法，以高渲染质量和速度见长。然而，其庞大的数据需求为实际应用带来了挑战。在本文中，我们提出了一种高效的压缩技术，通过紧凑表示显著减少存储开销。 我们设计了一种结合点云数据和特征平面的统一架构，采用渐进式三平面结构（progressive tri-plane structure）。该方法利用 2D 特征平面实现连续的空间表示。为了进一步优化这些表示，我们在频域中引入了针对标准视频编解码器设计的熵建模，并提出了通道级比特分配方法，以在比特率消耗和特征平面表示之间取得更好的平衡。 因此，我们的模型能够有效利用特征平面内的空间相关性，通过标准的非可微视频编解码器提升率失真性能。实验结果表明，该方法在数据紧凑性上优于现有方法，同时保持了高渲染质量。