Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …

Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …

In this paper, we aim at addressing two critical issues in the 3D detection task, including the
exploitation of multiple sensors (namely LiDAR point cloud and camera image), as well as …

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

In the field of monocular 3D detection, it is common practice to utilize scene geometric clues
to enhance the detector's performance. However, many existing works adopt these clues …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object
detection. Towards this goal we present an end-to-end learnable architecture that reasons …

Abstract 3D object detection is an essential task in autonomous driving. Recent techniques
excel with highly accurate detection rates, provided the 3D input data is obtained from …