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 …

Image guided depth completion is the task of generating a dense depth map from a sparse
depth map and a high quality image. In this task, how to fuse the color and depth modalities …

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 …

Generally, convolutional neural networks (CNNs) process data on a regular grid, eg, data
generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input …

Depth completion, a task to estimate the dense depth map from sparse measurement under
the guidance from the high-resolution image, is essential to many computer vision …

Image guided depth completion aims to recover per-pixel dense depth maps from sparse
depth measurements with the help of aligned color images, which has a wide range of …

Road extraction from remote sensing (RS) images in very high resolution is important for
autonomous driving and road planning. Compared with large-scale objects, roads are …

In a large-scale epidemic, reducing direct contact among medical personnel, attendants and
patients has become a necessary means of epidemic prevention and control. Intelligent …

RGBD images, combining high-resolution color and lower-resolution depth from various
types of depth sensors, are increasingly common. One can significantly improve the …

Human detection is a technology that detects human shapes in the image and ignores
everything else. However, modern person detectors have some inefficiencies in detecting …