  Occupancy prediction has increasingly garnered attention in recent years for
its fine-grained understanding of 3D scenes. Traditional approaches typically
rely on dense, regular grid representations, which often leads to excessive
computational demands and a loss of spatial details for small objects. This
paper introduces OctreeOcc, an innovative 3D occupancy prediction framework
that leverages the octree representation to adaptively capture valuable
information in 3D, offering variable granularity to accommodate object shapes
and semantic regions of varying sizes and complexities. In particular, we
incorporate image semantic information to improve the accuracy of initial
octree structures and design an effective rectification mechanism to refine the
octree structure iteratively. Our extensive evaluations show that OctreeOcc not
only surpasses state-of-the-art methods in occupancy prediction, but also
achieves a 15%-24% reduction in computational overhead compared to
dense-grid-based methods.
