  Video inpainting fills in corrupted video content with plausible
replacements. While recent advances in endoscopic video inpainting have shown
potential for enhancing the quality of endoscopic videos, they mainly repair 2D
visual information without effectively preserving crucial 3D spatial details
for clinical reference. Depth-aware inpainting methods attempt to preserve
these details by incorporating depth information. Still, in endoscopic
contexts, they face challenges including reliance on pre-acquired depth maps,
less effective fusion designs, and ignorance of the fidelity of 3D spatial
details. To address them, we introduce a novel Depth-aware Endoscopic Video
Inpainting (DAEVI) framework. It features a Spatial-Temporal Guided Depth
Estimation module for direct depth estimation from visual features, a Bi-Modal
Paired Channel Fusion module for effective channel-by-channel fusion of visual
and depth information, and a Depth Enhanced Discriminator to assess the
fidelity of the RGB-D sequence comprised of the inpainted frames and estimated
depth images. Experimental evaluations on established benchmarks demonstrate
our framework's superiority, achieving a 2% improvement in PSNR and a 6%
reduction in MSE compared to state-of-the-art methods. Qualitative analyses
further validate its enhanced ability to inpaint fine details, highlighting the
benefits of integrating depth information into endoscopic inpainting.
