Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

Multimodal object detection has attracted great attention in recent years since the
information specific to different modalities can complement each other and effectively …

Depth completion aims to recover pixelwise depth from incomplete and noisy depth
measurements with or without the guidance of a reference RGB image. This task attracted …

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 prediction from single image is a challenging task due to the intra scale ambiguity and
unavailability of prior information. The prediction of an unambiguous depth from single RGB …

Effectively detecting road objects in various environments would significantly improve
driving safety for autonomous vehicles. However, small objects, low illumination, and blurred …

Tree canopy attributes or characteristics, such as canopy volume and density, are important
parameters for calculating the precise quantity of agrochemicals required in each tree …

Light detection and ranging (LiDAR) technology's expansion within the autonomous
vehicles industry has rapidly motivated its application in numerous growing areas, such as …

Continuous health monitoring in private spaces such as the car is not yet fully exploited to
detect diseases in an early stage. Therefore, we develop a redundant health monitoring …

In autonomous driving, environment perception is the fundamental task for intelligent
vehicles which provides the necessary environment information for other applications. The …