Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

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 …

LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …

In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for
3D object detection. Because the camera and LiDAR sensor signals have different …

We propose a two-stage 3D object detection framework, named sparse-to-dense 3D Object
Detector (STD). The first stage is a bottom-up proposal generation network that uses raw …

Object detection in point clouds is an important aspect of many robotics applications such as
autonomous driving. In this paper, we consider the problem of encoding a point cloud into a …

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

Autonomous driving vehicles require both a precise localization and mapping solution in
different driving environment. In this context, Simultaneous Localization and Mapping …

In this paper we propose an approach for monocular 3D object detection from a single RGB
image, which leverages a novel disentangling transformation for 2D and 3D detection losses …