Depth completion, which combines additional sparse depth information from the range
sensors, substantially improves the accuracy of monocular depth estimation, especially …

With the wide application of sparse ToF sensors in mobile devices, RGB image-guided
sparse depth completion has attracted extensive attention recently, but still faces some …

This work considers the problem of depth completion, with or without image data, where an
algorithm may measure the depth of a prescribed limited number of pixels. The algorithmic …

Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth
map into a dense depth prediction. Due to its potentially rich semantic information, RGB …

Depth completion aims to recover dense depth maps from sparse depth maps using the
corresponding RGB images as guides. Learning guided convolutional network (GuideNet) is …

Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth
map into a dense depth prediction. The sparse depth map serves as a partial reference for …

Perceiving the 3D structure of the spacecraft is a prerequisite for successfully executing
many on-orbit space missions, and it can provide critical input for many downstream vision …

In this paper, we propose an end-to-end deep learning network named 3dDepthNet, which
produces an accurate dense depth image from a single pair of sparse LiDAR depth and …

Dense depth perception is critical for many applications. However, LiDAR sensors can only
provide sparse depth measurements. Therefore, completing the sparse LiDAR data …

LiDAR depth maps provide environmental guidance in a variety of applications. However,
such depth maps are typically sparse and insufficient for complex tasks such as autonomous …