Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

Autonomous driving faces great safety challenges for a lack of global perspective and the
limitation of long-range perception capabilities. It has been widely agreed that vehicle …

Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …

Click-based interactive image segmentation aims at extracting objects with a limited user
clicking. A hierarchical backbone is the de-facto architecture for current methods. Recently …

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-
standing problem in computer vision. Driven by end-to-end deep learning, recent studies …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

In this work, we present a conceptually simple yet effective framework for cross-modality 3D
object detection, named voxel field fusion. The proposed approach aims to maintain cross …

The Boreas dataset was collected by driving a repeated route over the course of 1 year,
resulting in stark seasonal variations and adverse weather conditions such as rain and …

It has been well recognized that fusing the complementary information from depth-aware
LiDAR point clouds and semantic-rich stereo images would benefit 3D object detection …