4 accepted papers
Guided depth completion aims to recover dense depth maps by propagating depth information from the given pixels to the remaining ones under the guidance of RGB images. However, most of the existing methods achieve this using a large number of iterative refinements or stacking repetitive blocks. Due…
We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the a…
To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guide…
We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator, the iterator of IHN has tied weights and is completely trainable. IHN achieves state…