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 …

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

Autonomous driving perceives its surroundings for decision making, which is one of the most
complex scenarios in visual perception. The success of paradigm innovation in solving the …

Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …

Monocular 3D object detection is an important yet challenging task in autonomous driving.
Some existing methods leverage depth information from an off-the-shelf depth estimator to …

Recognizing scenes and objects in 3D from a single image is a longstanding goal of
computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets …

Existing deep learning-based approaches for monocular 3D object detection in autonomous
driving often model the object as a rotated 3D cuboid while the object's geometric shape has …

Monocular 3D object detection aims to localize 3D bounding boxes in an input single 2D
image. It is a highly challenging problem and remains open, especially when no extra …

3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …