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 …

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

Autonomous vehicles rely extensively on perception systems to navigate and interpret their
surroundings. Despite significant advancements in these systems recently, challenges …

Perceiving the world as 3D occupancy supports embodied agents to avoid collision with any
types of obstacle. While open-vocabulary image understanding has prospered recently, how …

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

In this work, we introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an
approach which can predict high-quality assets with 512k Gaussians and 21 input images in …

The rise of large foundation models, trained on extensive datasets, is revolutionizing the
field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by …