π€ AI Summary
This study addresses the weak inter-frame correlation and low video coding efficiency in Gaussian Splatting sequence compression caused by the absence of tracking information. To overcome this limitation, we propose GSCV, whose core innovation lies in the Inter-PLAS mechanism, which replaces conventional PLAS to significantly enhance inter-frame similarity without requiring tracking data. Furthermore, a novel compression pipeline tailored for high-bit-depth standard video codecs is constructed. Experimental results demonstrate that the proposed method substantially outperforms both MPEG and point cloud baselines in terms of compression ratio and reconstruction quality, effectively breaking through the performance bottlenecks of existing 3D Gaussian video coding approaches.
π Abstract
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.