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.

OccFiner 是一个新颖的离线框架，旨在提高基于视觉的占位预测的准确性，通过多对多局部传播网络和区域中心全局传播两个混合阶段来解决几何和语义估计、视角变化连续性以及单视图遮挡等问题，从而在各种粗略占位类型上实现了几何和语义准确性的提升，创造了 SemanticKITTI 数据集上的最新性能记录，并超越了基于激光雷达的离线 SSC 模型的水平。