  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. However, operating on dense latent spaces introduces a
cubic time and space complexity, which limits scalability in terms of
perception range or spatial resolution. Existing approaches compress the dense
representation using projections like Bird's Eye View (BEV) or Tri-Perspective
View (TPV). Although efficient, these projections result in information loss,
especially for tasks like semantic occupancy prediction. To address this, we
propose SparseOcc, an efficient occupancy network inspired by sparse point
cloud processing. It utilizes a lossless sparse latent representation with
three key innovations. Firstly, a 3D sparse diffuser performs latent completion
using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature
pyramid and sparse interpolation enhance scales with information from others.
Finally, the transformer head is redesigned as a sparse variant. SparseOcc
achieves a remarkable 74.9% reduction on FLOPs over the dense baseline.
Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in
part can be attributed to the sparse representation's ability to avoid
hallucinations on empty voxels.
