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 …

We review solutions to the problem of depth estimation, arguably the most important subtask
in scene understanding. We focus on the single image depth estimation problem. Due to its …

We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular images …

Depth estimation from a single image is a fundamental problem in computer vision. In this
paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) …

In this paper, we propose the convolutional spatial propagation network (CSPN) and
demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and …

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 …

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 …

As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …

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 …

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 …