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 …

Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …

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

We propose a method to infer a dense depth map from a single image, its calibration, and
the associated sparse point cloud. In order to leverage existing models (teachers) that …

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

Unsupervised depth completion aims to recover dense depth from the sparse one without
using the ground-truth annotation. Although depth measurement obtained from LiDAR is …

In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as
panoramic 3D cameras often produce 360∘ depth with missing data in complex scenes. Its …

RGB-guided depth completion aims at predicting dense depth maps from sparse depth
measurements and corresponding RGB images, where how to effectively and efficiently …

Pansharpening is a procedure that fuses high-resolution panchromatic (PAN) images and
low-resolution multispectral (LMS) images to derive high-resolution multispectral (HMS) …