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 …

Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

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 …

The raw depth image captured by the indoor depth sensor usually has an extensive range of
missing depth values due to inherent limitations such as the inability to perceive transparent …

With the development of computational intelligence algorithms, unsupervised monocular
depth and pose estimation framework, which is driven by warped photometric consistency …

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR
sensors produce highly sparse depth maps, which are also noisy around the object …

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 …

Cross-domain face translation aims to transfer face images from one domain to another. It
can be widely used in practical applications, such as photos/sketches in law enforcement …

Integrating an RGB camera into a ToF imaging system has become a significant technique
for perceiving the real world. The RGB guided ToF imaging system is crucial to several …

Guided filter is a fundamental tool in computer vision and computer graphics, which aims to
transfer structure information from the guide image to the target image. Most existing …