轻量级3D高斯散射压缩 via 视频编码
Lightweight 3D Gaussian Splatting Compression via Video Codec
AI总结：
本文提出了一种基于视频编码的轻量级3D高斯散射压缩方法，通过两阶段Morton扫描和MiniPLAS提升压缩效率，实现中低比特率下的性能提升。
摘要：
当前基于视频的GS压缩方法依赖于使用并行线性分配排序（PLAS）将3D GS转换为平滑的2D地图，这种方法计算成本高且耗时，限制了GS在轻量级设备上的应用。本文提出了一种基于视频编码的轻量级3D高斯散射压缩方法（LGSCV）。首先，提出了一种两阶段Morton扫描，以生成块状的2D地图，这些地图适合具有编码单元（CU）为正方形块的通用视频编码器。使用3D Morton扫描对GS原语进行排列，随后使用2D Morton扫描将有序的GS原语映射到2D地图，以块状方式。然而，尽管块状2D地图在高比特率区域的性能接近PLAS地图，但在中等至低比特率区域会出现质量崩溃。因此，使用主成分分析（PCA）来减少球面谐波（SH）的维度，并设计了一种灵活且快速的MiniPLAS，用于在特定块大小内排列原语。结合SH PCA和MiniPLAS，导致了在率失真（RD）性能上的显著提升，尤其是在中等和低比特率情况下。MiniPLAS还可以指导编码器CU大小配置，并显著减少编码时间。在MPEG数据集上的实验结果表明，所提出的LGSCV相比最先进的方法实现了超过20%的RD增益，同时将2D地图生成时间减少到约1秒，并将编码时间减少了50%。代码可在https://github.com/Qi-Yangsjtu/LGSCV上获得。
英文摘要：
Current video-based GS compression methods rely on using Parallel Linear Assignment Sorting (PLAS) to convert 3D GS into smooth 2D maps, which are computationally expensive and time-consuming, limiting the application of GS on lightweight devices. In this paper, we propose a Lightweight 3D Gaussian Splatting (GS) Compression method based on Video codec (LGSCV). First, a two-stage Morton scan is proposed to generate blockwise 2D maps that are friendly for canonical video codecs in which the coding units (CU) are square blocks. A 3D Morton scan is used to permute GS primitives, followed by a 2D Morton scan to map the ordered GS primitives to 2D maps in a blockwise style. However, although the blockwise 2D maps report close performance to the PLAS map in high-bitrate regions, they show a quality collapse at medium-to-low bitrates. Therefore, a principal component analysis (PCA) is used to reduce the dimensionality of spherical harmonics (SH), and a MiniPLAS, which is flexible and fast, is designed to permute the primitives within certain block sizes. Incorporating SH PCA and MiniPLAS leads to a significant gain in rate-distortion (RD) performance, especially at medium and low bitrates. MiniPLAS can also guide the setting of the codec CU size configuration and significantly reduce encoding time. Experimental results on the MPEG dataset demonstrate that the proposed LGSCV achieves over 20% RD gain compared with state-of-the-art methods, while reducing 2D map generation time to approximately 1 second and cutting encoding time by 50%. The code is available at https://github.com/Qi-Yangsjtu/LGSCV .
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。
