基于混合先验的3D Gaussian Splatting数据压缩
3D Gaussian Splatting Data Compression with Mixture of Priors
AI总结：
针对3DGS数据压缩中熵模型不足与量化策略次优的问题，提出混合先验策略，通过门控机制融合轻量级MLP生成的特征以改进条件熵建模，并利用先验引导的由粗到细量化策略增强有损压缩，在多个基准上达到SOTA。
摘要：
3D Gaussian Splatting (3DGS)数据压缩对于在3D场景建模中实现高效存储和传输至关重要。然而，由于在无损和有损压缩场景中缺乏充分的熵模型和次优的量化策略，其发展仍然受限，现有方法尚未1) 充分利用超先验信息来构建稳健的条件熵模型，以及2) 应用细粒度的逐元素量化策略以提高压缩粒度。在本工作中，我们提出了一种新颖的混合先验策略来同时解决这两个挑战。具体而言，受混合专家范式启发，我们的MoP方法通过多个轻量级MLP处理超先验信息以生成多样化的先验特征，随后通过门控机制将其集成为MoP特征。为了增强无损压缩，生成的MoP特征被用作超先验以改进条件熵建模。同时，对于有损压缩，我们在逐元素量化过程中将MoP特征用作引导信息，采用具有预定义量化步长值的先验引导的由粗到细量化策略。具体来说，我们将量化步长值扩展为矩阵，并在MoP特征的引导下自适应地将其从粗粒度细化到细粒度，从而获得便于逐元素量化的量化步长矩阵。大量实验表明，我们提出的3DGS数据压缩框架在多个基准上实现了最先进的性能，包括Mip-NeRF360、BungeeNeRF、DeepBlending和Tank&Temples。
英文摘要：
3D Gaussian Splatting (3DGS) data compression is crucial for enabling efficient storage and transmission in 3D scene modeling. However, its development remains limited due to inadequate entropy models and suboptimal quantization strategies for both lossless and lossy compression scenarios, where existing methods have yet to 1) fully leverage hyperprior information to construct robust conditional entropy models, and 2) apply fine-grained, element-wise quantization strategies for improved compression granularity. In this work, we propose a novel Mixture of Priors (MoP) strategy to simultaneously address these two challenges. Specifically, inspired by the Mixture-of-Experts (MoE) paradigm, our MoP approach processes hyperprior information through multiple lightweight MLPs to generate diverse prior features, which are subsequently integrated into the MoP feature via a gating mechanism. To enhance lossless compression, the resulting MoP feature is utilized as a hyperprior to improve conditional entropy modeling. Meanwhile, for lossy compression, we employ the MoP feature as guidance information in an element-wise quantization procedure, leveraging a prior-guided Coarse-to-Fine Quantization (C2FQ) strategy with a predefined quantization step value. Specifically, we expand the quantization step value into a matrix and adaptively refine it from coarse to fine granularity, guided by the MoP feature, thereby obtaining a quantization step matrix that facilitates element-wise quantization. Extensive experiments demonstrate that our proposed 3DGS data compression framework achieves state-of-the-art performance across multiple benchmarks, including Mip-NeRF360, BungeeNeRF, DeepBlending, and Tank&Temples.
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。
