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 …

Depth completion plays a crucial role in autonomous driving, in which cameras and LiDARs
are two complementary sensors. Recent approaches attempt to exploit spatial geometric …

Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

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 …

Guided depth completion aims to recover dense depth maps by propagating depth
information from the given pixels to the remaining ones under the guidance of RGB images …

Existing deep learning-based depth completion methods generally employ massive stacked
layers to predict the dense depth map from sparse input data. Although such approaches …

Depth Completion deals with the problem of converting a sparse depth map to a dense one,
given the corresponding color image. Convolutional spatial propagation network (CSPN) is …

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 …

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 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 …