一种用于3D Gaussian Splatting压缩的分层压缩技术
A Hierarchical Compression Technique for 3D Gaussian Splatting Compression
AI总结：
针对3D Gaussian Splatting数据本身压缩不足的问题，提出HGSC，通过重要性剪枝、Octree、KD-tree分块及LoD锚点预测实现显著压缩。
摘要：
3D Gaussian Splatting（GS）在新视角合成中展现出优异的渲染质量和生成速度。然而，庞大的数据规模给存储和传输带来挑战，使3D GS压缩成为一项关键技术。当前3D GS压缩研究主要集中于开发更紧凑的场景表示，例如将显式3D GS数据转换为隐式形式。相比之下，对GS数据本身的压缩几乎尚未得到探索。为弥补这一空白，我们提出一种分层GS压缩（Hierarchical GS Compression，HGSC）技术。首先，我们依据来自全局和局部重要性的重要性分数剪枝不重要的Gaussian，在保持视觉质量的同时有效减少冗余。采用Octree结构压缩3D位置。在3D GS Octree基础上，我们通过使用KD-tree将3D GS划分为多个块，实施分层属性压缩策略。我们应用最远点采样在每个块内选择锚基元，并将其他基元作为具有不同细节层次（Levels of Details，LoDs）的非锚基元。锚基元充当参考点，用于预测跨不同LoD的非锚基元，以减少空间冗余。对于锚基元，我们使用区域自适应分层变换实现多种属性的近无损压缩。对于非锚基元，每个基元都基于k个最近锚基元进行预测。为进一步最小化预测误差，将重建后的LoD与锚基元结合，形成新的锚基元以预测下一个LoD。在小场景数据集上，与最先进压缩方法相比，我们的方法显著实现了更优的压缩质量和超过4.5倍的数据规模缩减。
英文摘要：
3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compact scene representations, such as converting explicit 3D GS data into implicit forms. In contrast, compression of the GS data itself has hardly been explored. To address this gap, we propose a Hierarchical GS Compression (HGSC) technique. Initially, we prune unimportant Gaussians based on importance scores derived from both global and local significance, effectively reducing redundancy while maintaining visual quality. An Octree structure is used to compress 3D positions. Based on the 3D GS Octree, we implement a hierarchical attribute compression strategy by employing a KD-tree to partition the 3D GS into multiple blocks. We apply farthest point sampling to select anchor primitives within each block and others as non-anchor primitives with varying Levels of Details (LoDs). Anchor primitives serve as reference points for predicting non-anchor primitives across different LoDs to reduce spatial redundancy. For anchor primitives, we use the region adaptive hierarchical transform to achieve near-lossless compression of various attributes. For non-anchor primitives, each is predicted based on the k-nearest anchor primitives. To further minimize prediction errors, the reconstructed LoD and anchor primitives are combined to form new anchor primitives to predict the next LoD. Our method notably achieves superior compression quality and a significant data size reduction of over 4.5 times compared to the state-of-the-art compression method on small scenes datasets.
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