Plenoptic Super-Resolution via Hybrid Light-Field and 3D Gaussian Priors

Plenoptic super-resolution aims to reconstruct a scene at higher spatial-angular resolution from sparse, low-resolution light-field (LF) observations. Prior methods rely on spatio-angular priors like epipolar-plane image (EPI) continuity, but these can break under wide baselines, large disparities, and occlusions, and most models are tied to fixed upsampling factors, limiting resolution control at inference. To overcome these limitations, we complement LF structural priors with an explicit 3D Gaussian representation, which naturally supports continuous angular rendering and occlusion-aware view synthesis. Building on these complementary LF and 3DGS priors, we propose PlenoGS, a two-stage framework for plenoptic super-resolution. The first stage reconstructs a stable 3D Gaussian representation regularized by LF priors. The second stage enriches this representation with high-resolution spatial details through geometry-consistent refinement and scale-aware local implicit decoding, thereby preserving consistency across viewpoints while supporting flexible spatial magnification. On synthetic and real wide-baseline LF images, PlenoGS reduces occlusion ghosting and grid artifacts and improves spatial detail, angular consistency, and novel-view quality.