Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …

In this paper, we consider convolutional neural networks operating on sparse inputs with an
application to depth completion from sparse laser scan data. First, we show that traditional …

In this paper, we propose a deep learning architecture that produces accurate dense depth
for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor …

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 …

The goal of our work is to complete the depth channel of an RGB-D image. Commodity-
grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant …

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

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 …

Modern camera calibration and multiview stereo techniques enable users to smoothly
navigate between different views of a scene captured using standard cameras. The …

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 …

In this paper we consider the problem of estimating a dense depth map from a set of sparse
LiDAR points. We use techniques from compressed sensing and the recently developed …