SpatioTemporal Difference Network for Video Depth Super-Resolution

The introduction of the SpatioTemporal Difference Network (STDNet) represents a significant advancement in video depth super-resolution, a field that has struggled with long-tailed distributions affecting reconstruction quality. By employing a dual-branch approach, STDNet effectively tackles the challenges of spatial non-smooth regions and temporal variations. The spatial difference branch aligns RGB features with learned representations to enhance depth calibration, while the temporal difference branch propagates information from adjacent frames to improve depth accuracy. Extensive experimental results demonstrate that STDNet outperforms existing methods, showcasing its potential to redefine standards in depth reconstruction. As the demand for high-quality video processing continues to grow, innovations like STDNet are crucial for advancing technologies in computer vision and related applications.