4 accepted papers
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal inconsistency. To address this, we propose Unified Video Fusion (UniVF), a novel and unified framework for video fusion that l…
Depth completion upgrades sparse depth measurements into dense depth maps, guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings, and tend to struggle when applied to images outside the training domain, as well as when the available d…
By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although recent diffusion-based MDE approaches exhibit a superior ability to extract details, they struggle in geometrically comple…
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimator…