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 …

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 …

Depth completion has been widely studied to predict a dense depth image from its sparse
measurement and a single color image. However, most state-of-the-art methods rely on …

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 …

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 …

In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal,
we design a simple yet effective neural network block that learns to extract joint 2D and 3D …

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 …

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 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 …

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …