Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …

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 …

We propose a deep neural network architecture to infer dense depth from an image and a
sparse point cloud. It is trained using a video stream and corresponding synchronized …

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 …

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 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 …

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