English(EN) RacerGS: Residual-Autoregressive Context for 3D Gaussian Splatting Compression

RacerGS框架为3D高斯溅射模型实现超过150倍的压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了RacerGS，一个用于压缩3D高斯溅射（3DGS）模型的新颖框架。该方法在保持高质量渲染的同时，显著减小了3DGS场景的存储大小。RacerGS采用哈希投影模型来获取紧凑的上下文特征，并使用FiLM调制的自回归GRU进行高效比特率编码。与标准的3DGS相比，该框架实现了超过150倍的压缩，与Scaffold-GS相比实现了28倍的压缩，在存储减小和率失真性能方面优于现有的压缩技术。 AI

影响该压缩框架可能会显著降低3D渲染应用的存储和传输成本，从而可能加速其采用。

排序理由这是一篇详细介绍一种特定计算机视觉技术新压缩框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Wenqing Wang, Huimin Zeng, Yun Fu · 2026-10-27 04:00

RacerGS: 3D高斯溅射压缩的残差自回归上下文

arXiv:2610.34263v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising approach for fast, high-quality novel-view rendering. However, its large parameter size remains a bottleneck for storage and transmission. We propose RacerGS, a compact 3DGS co…

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RacerGS: 3D高斯溅射压缩的残差自回归上下文

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