分层渐进式高斯点云，实现图像视频的高质量可扩展重建
P-GSVC: Layered Progressive 2D Gaussian Splatting for Scalable Image and Video
- 将高斯点分层组织，支持从粗到精的渐进式重建
- 联合优化多层高斯点，视频PSNR提升1.9dB，图像提升2.6dB
- 适合需要高分辨率与高质量重建的视觉生成任务
高斯点云已成为图像和视频重建中一种有竞争力的显式表示方法。本文提出P-GSVC，首个面向图像与视频的分层渐进式2D高斯点云框架，实现了高斯表示的统一可扩展方案。P-GSVC将2D高斯点划分为基础层与后续增强层，支持从粗到精的重建过程。为有效优化该分层结构，我们提出一种联合训练策略，同步更新各层高斯点，对齐其优化轨迹，确保层间兼容性与稳定渐进重建。该方法在质量与分辨率上均具备可扩展性。实验表明，相比逐层顺序训练，联合训练在视频上可实现最高1.9 dB的PSNR提升，在图像上可达2.6 dB提升。
原文摘要 · Abstract (English)
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/
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