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 …

Abstract 3D occupancy prediction is an important task for the robustness of vision-centric
autonomous driving which aims to predict whether each point is occupied in the surrounding …

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 …

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

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 …

Vision-based perception for autonomous driving requires an explicit modeling of a 3D space
where 2D latent representations are mapped and subsequent 3D operators are applied …

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 …

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 …