P-GSVC: Layered Progressive 2D Gaussian Splatting for Scalable Image and Video

TL;DR

P-GSVC introduces a layered progressive Gaussian splatting framework that enables scalable, high-quality image and video reconstruction through joint training of layered Gaussian representations.

Contribution

It is the first layered progressive 2D Gaussian splatting method that unifies scalable image and video representation with joint optimization for improved quality.

Findings

Up to 1.9 dB PSNR improvement in videos

Up to 2.6 dB PSNR improvement in images

Supports scalable quality and resolution

Abstract

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/