Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

Depth completion deals with the problem of recovering dense depth maps from sparse ones,
where color images are often used to facilitate this task. Recent approaches mainly focus on …

Depth completion aims to recover a dense depth map from the sparse depth data and the
corresponding single RGB image. The observed pixels provide the significant guidance for …

Depth completion aims to recover pixelwise depth from incomplete and noisy depth
measurements with or without the guidance of a reference RGB image. This task attracted …

Depth completion aims to recover a dense depth map from a sparse depth map with the
corresponding color image as input. Recent approaches mainly formulate the depth …

In this paper, we tackle the problem of dynamic scene deblurring. Most existing deep end-to-
end learning approaches adopt the same generic model for all unseen test images. These …

Depth completion has been widely studied to predict a dense depth image from its sparse
measurement and a single color image. However, most state-of-the-art methods rely on …

Depth completion involves recovering a dense depth map from a sparse map and an RGB
image. Recent approaches focus on utilizing color images as guidance images to recover …

Inferring the depth of images is a fundamental inverse problem within the field of Computer
Vision since depth information is obtained through 2D images, which can be generated from …

Depth information is a significant ingredient to visually perceive the physical world.
However, mainstream depth sensors, eg, time-of-flight (ToF) cameras, often measure …