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 …

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

The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and
estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation …

3D semantic occupancy prediction is an important task for robust vision-centric autonomous
driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most …

3D occupancy prediction is important for autonomous driving due to its comprehensive
perception of the surroundings. To incorporate sequential inputs, most existing methods fuse …

3D Semantic Occupancy Prediction is fundamental for spatial understanding as it provides a
comprehensive semantic cognition of surrounding environments. However, prevalent …

3D occupancy perception is gaining increasing attention due to its capability to offer detailed
and precise environment representations. Previous weakly-supervised NeRF methods …

The field of autonomous driving is experiencing a surge of interest in world models, which
aim to predict potential future scenarios based on historical observations. In this paper, we …

Vision-based semantic occupancy and flow prediction plays a crucial role in providing
spatiotemporal cues for real-world tasks, such as autonomous driving. Existing methods …

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in
Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of …