In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities
with complementary cues for 3D object detection. However, it is quite difficult to sufficiently …

The pursuit of autonomous driving relies on developing perception systems capable of
making accurate, robust, and rapid decisions to interpret the driving environment effectively …

Autonomous driving requires accurate, robust, and fast decision-making perception systems
to understand the driving environment. Object detection is critical in allowing the perception …

While numerous 3D detection works leverage the complementary relationship between RGB
images and point clouds, developments in the broader framework of semi-supervised object …

One of the major components of the neural network, the feature pyramid plays a vital part in
perception tasks, like object detection in autonomous driving. But it is a challenge to fuse …

Recently, 3D object detection models have achieved very good performance under normal
weather conditions, with the SE-SSD model having produced the highest performance by …

Advancements in deep neural networks have made it a prominent approach for most of the
complex computer vision tasks. A key aspect for the deployment of deep neural networks in …

Recently significant progress has been made in 3D detection. However, it is still challenging
to detect small contour objects under complex scenes. This paper proposes a novel …

DNNs have been excelling on many computer vision tasks, achieving many milestones and
are continuing to prove their validity. It is important for DNNs to express their uncertainties to …

Current learning-based 3-D object detection accuracy is heavily impacted by the annotation
quality. It is still a challenge to expect an overall high detection accuracy for all classes …