Search papers, labs, and topics across Lattice.
The paper introduces P-GSVC, a layered progressive 2D Gaussian splatting framework for scalable image and video reconstruction. It organizes Gaussian splats into a base layer and enhancement layers for coarse-to-fine reconstruction. A joint training strategy is proposed to simultaneously update Gaussians across layers, improving inter-layer compatibility and reconstruction quality, achieving up to 1.9 dB PSNR improvement for video and 2.6 dB for images compared to sequential training.
Jointly training layered Gaussian splats boosts reconstruction quality by up to 2.6 dB, proving that coordinating optimization across layers is key for progressive 2D Gaussian splatting.
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/
NUS