Image guided depth completion is the task of generating a dense depth map from a sparse
depth map and a high quality image. In this task, how to fuse the color and depth modalities …

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 …

Depth completion, the technique of estimating a dense depth image from sparse depth
measurements, has a variety of applications in robotics and autonomous driving. However …

This work proposes a new method to accurately complete sparse LiDAR maps guided by
RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in …

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

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 …

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 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 …

We present a deep learning system to infer the posterior distribution of a dense depth map
associated with an image, by exploiting sparse range measurements, for instance from a …