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 …

Successful depth completion from a single RGB-D image requires both extracting plentiful
2D and 3D features and merging these heterogeneous features appropriately. We propose …

We present a method for inferring dense depth maps from images and sparse depth
measurements by leveraging synthetic data to learn the association of sparse point clouds …

Depth completion recovers dense depth from sparse measurements, eg, LiDAR. Existing
depth-only methods use sparse depth as the only input. However, these methods may fail to …

Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite
the popularity of color-guided methods for sparse-to-dense depth completion, they treated …

This paper proposes a novel framework integrating the principles of active stereo in
standard passive camera systems without a physical pattern projector. We virtually project a …

Depth Completion can produce a dense depth map from a sparse input and provide a more
complete 3D description of the environment. Despite great progress made in depth …

The basic framework of depth completion is to predict a pixel-wise dense depth map using
very sparse input data. In this paper, we try to solve this problem in a more effective way, by …

Inferring the depth of images is a fundamental inverse problem within the field of Computer
Vision since depth information is obtained through 2D images, which can be generated from …

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 …