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 …

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 …

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 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 …

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 …

Abstract 3D occupancy perception technology aims to observe and understand dense 3D
environments for autonomous vehicles. Owing to its comprehensive perception capability …

In recent years, 3D object perception has become a crucial component in the development
of autonomous driving systems, providing essential environmental awareness. However, as …

A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and
recent models for 3D semantic occupancy prediction have successfully addressed the …

Depth completion is a vital task for autonomous driving as it involves reconstructing the
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …

Vision-based Semantic Scene Completion (SSC) has gained much attention due to its
widespread applications in various 3D perception tasks. Existing sparse-to-dense …