LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

With the improvements in sensor performance (cameras, Lidars) and the application of deep
learning in object detection, autonomous vehicles (AVs) are gradually becoming more …

The point clouds scanned by lidar are generally sparse, which can result in fewer sampling
points of objects. To perform precise and effective 3D object detection, it is necessary to …

Abstract 3D object detection plays a vital role and exerts a growing important effect in many
applications, such as 3D scene understanding and autonomous driving. In this paper, we …

Environmental perception obtained via object detectors have no predictable safety layer
encoded into their model schema, which creates the question of trustworthiness about the …

Energy-based models (EBMs) have experienced a resurgence within machine learning in
recent years, including as a promising alternative for probabilistic regression. However …

Recently, 3D human pose estimation has attracted more attention due to its promising
applications. In general, existing methods usually directly predict a target 3D pose for a …

The utilization of LiDAR point cloud in various applications, such as autonomous driving and
intelligent transportation, has gained significant attention and become a research hotspot …

The spatiotemporal constraints provided by loop closure detection play a crucial role in the
simultaneous localization and map** of robots. Traditional methods based on key points …