Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

The capabilities of autonomous mobile robotic systems have been steadily improving due to
recent advancements in computer science, engineering, and related disciplines such as …

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 …

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 …

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 aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

Depth completion is a vital task for autonomous driving as it involves reconstructing the
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …

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

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 …