Robotic perception requires the modeling of both 3D geometry and semantics. Existing
methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details …

Grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, grid-centric perception is less prevalent than object-centric perception for …

Human driver can easily describe the complex traffic scene by visual system. Such an ability
of precise perception is essential for driver's planning. To achieve this, a geometry-aware …

Object detection, a fundamental and challenging problem in computer vision, has
experienced rapid development due to the effectiveness of deep learning. The current …

Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …

Comprehensive modeling of the surrounding 3D world is crucial for the success of
autonomous driving. However existing perception tasks like object detection road structure …

3D occupancy prediction holds significant promise in the fields of robot perception and
autonomous driving, which quantifies 3D scenes into grid cells with semantic labels. Recent …

The autonomous driving community has shown significant interest in 3D occupancy
prediction driven by its exceptional geometric perception and general object recognition …

Collaborative perception in automated vehicles leverages the exchange of information
between agents aiming to elevate perception results. Previous camera-based collaborative …

In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …