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

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

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 …

Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …

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 …

Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …

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 …

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 …

Abstract 3D semantic occupancy prediction aims to obtain 3D fine-grained geometry and
semantics of the surrounding scene and is an important task for the robustness of vision …

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