发表机构
University of Missouri - Kansas City; Shanghai Jiaotong University; University of Canterbury; Qualcomm（密苏里大学堪萨斯城分校; 上海交通大学; 坎特伯雷大学; 高通公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种利用标准视频编解码器的高斯溅射序列压缩方法，通过Inter-PLAS增强帧间相关性，实现更高压缩率与质量上限。
AI 中文摘要
本文提出了一种新颖且有效的高斯溅射（GS）序列压缩方法，该方法利用视频编解码器（GSCV）。现有的基于视频的GS序列压缩依赖于并行线性分配排序（PLAS）和跟踪的图元信息，将GS转换为平滑的2D视频。然而，对于大多数实际应用，跟踪信息不可用，并且在没有跟踪信息的情况下，使用原始PLAS可能生成帧间相关性较弱的图像，这归因于其随机性。GSCV引入了一种简单而高效的Inter-PLAS方法，以在GS的I帧和P帧之间生成接近的图像，从而极大地增强了视频编解码器的帧间性能。GSCV还实现了一种基于最先进视频编解码器和高位深GS图像的新流程，实现了更高的压缩率，同时提供了更高的质量上限。实验结果表明，所提出的GSCV在GS序列压缩中相比MPEG视频和基于点云的锚点表现出明显改进的性能。代码可在以下网址获取：此https URL。
英文摘要
This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS sequence compression relies on the Parallel Linear Assignment Sorting (PLAS) and tracked primitive information to convert GS into smooth 2D videos. However, tracked information is not available for most practical applications, and without it, using the vanilla PLAS can generate images exhibiting weak inter-frame correlation, due to its stochastic nature. GSCV incorporates a simple yet efficient Inter-PLAS method to produce close images between the I- and P-frames of GS, enhancing the inter-frame performance of video codec greatly. GSCV also realizes a new pipeline based on the state-of-the-art video codecs with high bit-depth GS images, achieving higher compressibility while simultaneously providing a higher quality upper bound. Experimental results show that the proposed GSCV exhibits obviously improved performance over MPEG video and point cloud-based anchors in GS sequence compression. The code is available at https://github.com/Qi-Yangsjtu/GSCV.
CommentsAccepted by MM Asia 2026
