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 …

Scalable sensor simulation is an important yet challenging open problem for safety-critical
domains such as self-driving. Current works in image simulation either fail to be …

We present a method for inferring dense depth from a camera image and a sparse noisy
radar point cloud. We first describe the mechanics behind mmWave radar point cloud …

Depth completion starts from a sparse set of known depth values and estimates the
unknown depths for the remaining image pixels. Most methods model this as depth …

The focus in deep learning research has been mostly to push the limits of prediction
accuracy. However, this was often achieved at the cost of increased complexity, raising …

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 …

Majority of the perception methods in robotics require depth information provided by RGB-D
cameras. However, standard 3D sensors fail to capture depth of transparent objects due to …

Image guided depth completion aims to recover per-pixel dense depth maps from sparse
depth measurements with the help of aligned color images, which has a wide range of …

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 …

Modern self-driving perception systems have been shown to improve upon processing
complementary inputs such as LiDAR with images. In isolation, 2D images have been found …