  In recent years, autonomous driving has garnered escalating attention for its
potential to relieve drivers' burdens and improve driving safety. Vision-based
3D occupancy prediction, which predicts the spatial occupancy status and
semantics of 3D voxel grids around the autonomous vehicle from image inputs, is
an emerging perception task suitable for cost-effective perception system of
autonomous driving. Although numerous studies have demonstrated the greater
advantages of 3D occupancy prediction over object-centric perception tasks,
there is still a lack of a dedicated review focusing on this rapidly developing
field. In this paper, we first introduce the background of vision-based 3D
occupancy prediction and discuss the challenges in this task. Secondly, we
conduct a comprehensive survey of the progress in vision-based 3D occupancy
prediction from three aspects: feature enhancement, deployment friendliness and
label efficiency, and provide an in-depth analysis of the potentials and
challenges of each category of methods. Finally, we present a summary of
prevailing research trends and propose some inspiring future outlooks. To
provide a valuable reference for researchers, a regularly updated collection of
related papers, datasets, and codes is organized at
https://github.com/zya3d/Awesome-3D-Occupancy-Prediction.
