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 …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …

LiDAR-camera fusion methods have shown impressive performance in 3D object detection.
Recent advanced multi-modal methods mainly perform global fusion, where image features …

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This
representation mimics the well-studied image-based 2D bounding-box detection but comes …

There have been two streams in the 3D detection from point clouds: single-stage methods
and two-stage methods. While the former is more computationally efficient, the latter usually …

The past decade has witnessed the rapid development of autonomous driving systems.
However, it remains a daunting task to achieve full autonomy, especially when it comes to …

Existing offboard 3D detectors always follow a modular pipeline design to take advantage of
unlimited sequential point clouds. We have found that the full potential of offboard 3D …

Autonomous vehicles operate in highly dynamic environments necessitating an accurate
assessment of which aspects of a scene are moving and where they are moving to. A …

To safely deploy autonomous vehicles, onboard perception systems must work reliably at
high accuracy across a diverse set of environments and geographies. One of the most …