Abstract Machine learning (ML) has revolutionized robotics by enhancing perception,
adaptability, decision-making and more, enabling robots to work in complex scenarios …

With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall
into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is …

Monocular 3D object detection has received increasing attention due to the wide application
in autonomous driving. Existing works mainly focus on introducing geometry projection to …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Depth estimation from a single image is an important task that can be applied to various
fields in computer vision, and has grown rapidly with the development of convolutional …

In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Monocular 3D detection has drawn much attention from the community due to its low cost
and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space …

Multi-frame depth estimation improves over single-frame approaches by also leveraging
geometric relationships between images via feature matching, in addition to learning …

As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …