Latent Bridge Trajectory Distillation for 3D Super-Resolution

Synthesizing high-resolution (HR) novel views from given low-resolution (LR) observations with known camera poses is intrinsically ill-posed. Existing 3D Gaussian Splatting (3DGS) super-resolution (SR) methods typically incorporate pretrained restoration priors into the optimization process. However, conventional diffusion-based restoration methods follow a noise-to-HR trajectory that is poorly aligned with the 3DGS optimization initialized from LR observations, and HR details inferred independently across views can be inconsistent. To address these issues, we propose Latent Bridge Trajectory Distillation (LBTD), which progressively distills restoration knowledge along an LR-anchored restoration trajectory into the 3D representation. Specifically, a multi-view latent bridge transports LR observations directly toward the HR distribution, while a cross-view adapter enables pose-nearest views to exchange complementary information for more consistent detail recovery. Trajectory distillation then maintains a persistent bridge trajectory across successive 3DGS optimization stages, where clean HR images are estimated at intermediate steps and updated 3D renderings are fed back to advance the trajectory. For detail enhancement, we propose detail-aware Gaussian splitting, which allocates finer primitives along directions where SR supervision reveals structures beyond the evidence available in the LR observations. Experiments show that LBTD achieves state-of-the-art performance, outperforming 3DSR by 0.31–0.89 dB in PSNR while using fewer Gaussians. In addition, LBTD recovers richer details with fewer distortions, leading to sharper and more faithful HR renderings.