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 …

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 …

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 …

Dense depth cues are important and have wide applications in various computer vision
tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements …

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 …

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 has attracted extensive attention recently due to the development of
autonomous driving, which aims to recover dense depth map from sparse depth …

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 …

Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense 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 …