P-GSVC: Layered Progressive 2D Gaussian Splatting for Scalable Image and Video
This work addresses scalability in quality and resolution for image and video reconstruction, representing an incremental advancement in Gaussian splatting methods.
The paper tackles the problem of scalable Gaussian representation for image and video reconstruction by introducing P-GSVC, a layered progressive 2D Gaussian splatting framework, resulting in up to 2.6 dB PSNR improvement for images and 1.9 dB for videos compared to sequential methods.
Gaussian splatting has emerged as a competitive explicit representation for image and video reconstruction. In this work, we present P-GSVC, the first layered progressive 2D Gaussian splatting framework that provides a unified solution for scalable Gaussian representation in both images and videos. P-GSVC organizes 2D Gaussian splats into a base layer and successive enhancement layers, enabling coarse-to-fine reconstructions. To effectively optimize this layered representation, we propose a joint training strategy that simultaneously updates Gaussians across layers, aligning their optimization trajectories to ensure inter-layer compatibility and a stable progressive reconstruction. P-GSVC supports scalability in terms of both quality and resolution. Our experiments show that the joint training strategy can gain up to 1.9 dB improvement in PSNR for video and 2.6 dB improvement in PSNR for image when compared to methods that perform sequential layer-wise training. Project page: https://longanwang-cs.github.io/PGSVC-webpage/