  Vision-based occupancy prediction, also known as 3D Semantic Scene Completion
(SSC), presents a significant challenge in computer vision. Previous methods,
confined to onboard processing, struggle with simultaneous geometric and
semantic estimation, continuity across varying viewpoints, and single-view
occlusion. Our paper introduces OccFiner, a novel offboard framework designed
to enhance the accuracy of vision-based occupancy predictions. OccFiner
operates in two hybrid phases: 1) a multi-to-multi local propagation network
that implicitly aligns and processes multiple local frames for correcting
onboard model errors and consistently enhancing occupancy accuracy across all
distances. 2) the region-centric global propagation, focuses on refining labels
using explicit multi-view geometry and integrating sensor bias, especially to
increase the accuracy of distant occupied voxels. Extensive experiments
demonstrate that OccFiner improves both geometric and semantic accuracy across
various types of coarse occupancy, setting a new state-of-the-art performance
on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC
models to a level even surpassing that of LiDAR-based onboard SSC models.
Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a
purely vision-based approach. Quantitative experiments prove that OccFiner
successfully facilitates occupancy data loop-closure in autonomous driving.
Additionally, we quantitatively and qualitatively validate the superiority of
the offboard approach on city-level SSC static maps. The source code will be
made publicly available at https://github.com/MasterHow/OccFiner.
