Performing super-resolution of a depth image using the guidance from an RGB image is a
problem that concerns several fields, such as robotics, medical imaging, and remote …

We introduce a novel formulation for guided super-resolution. Its core is a differentiable
optimisation layer that operates on a learned affinity graph. The learned graph potentials …

Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D
reconstructions. However, the use of DNNs in the context of 3D reconstruction from large …

Under-display imaging has recently received considerable attention in both academia and
industry. As a variation of this technique, under-display ToF (UD-ToF) cameras enable depth …

Face recognition can benefit from the utilization of depth data captured using low-cost
cameras, in particular for presentation attack detection purposes. Depth video output from …

In this paper, a complete pipeline for image-based 3D reconstruction of urban scenarios is
proposed, based on PatchMatch Multi-View Stereo (MVS). Input images are firstly fed into an …

Consumer-level RGB-D cameras have been widely used for dense 3D reconstruction of
scenes. Especially for textureless or non-lambertian surfaces, consumer RGB-D cameras …

We propose a framework that combines traditional, hand-crafted algorithms and recent
advances in deep learning to obtain high-quality, high-resolution disparity maps from stereo …

Modern cameras are equipped with a wide array of sensors that enable recording the
geospatial context of an image. Taking advantage of this, we explore depth estimation under …

Deep learning approach has achieved great success in monocular depth estimation.
However, the learned deep network may produce a depth map with fewer details and …