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 …

High-definition (HD) semantic maps are a crucial component for autonomous driving on
urban streets. Traditional offline HD maps are created through labor-intensive manual …

We explore long-term temporal visual correspondence-based optimization for 3D video
object detection in this work. Visual correspondence refers to one-to-one mappings for …

In autonomous driving, there is growing interest in end-to-end online vectorized map
perception in bird's-eye-view (BEV) space, with an expectation that it could replace …

Autonomous vehicles rely extensively on perception systems to navigate and interpret their
surroundings. Despite significant advancements in these systems recently, challenges …

Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based
detectors have achieved impressive performance, the high cost of LiDAR sensors precludes …

Can knowing where you are assist in perceiving objects in your surroundings, especially
under adverse weather and lighting conditions? In this work we investigate whether a prior …

Humans naturally retain memories of permanent elements, while ephemeral moments often
slip through the cracks of memory. This selective retention is crucial for robotic perception …

The rapid development of 3D object detection systems for self-driving cars has significantly
improved accuracy. However, these systems struggle to generalize across diverse driving …

For a self-driving car to operate reliably, its perceptual system must generalize to the end-
user's environment---ideally without additional annotation efforts. One potential solution is to …