Vision-based occupancy networks provide an end-to-end solution for reconstructing the surrounding environment using semantic occupied voxels derived from multi-view images. This technique relies on effectively learning the correlation between pixel-level visual information and voxels. Despite recent advancements, occupancy results still suffer from limited accuracy due to occlusions and sparse visual cues. To address this, we propose a Lightweight Spatio-Temporal Correlation (LEAP)} method, which significantly enhances the performance of existing occupancy networks with minimal computational overhead. LEAP can be seamlessly integrated into various baseline networks, enabling a plug-and-play application. LEAP operates in three stages: 1) it tokenizes information from recent baseline and motion features into a shared, compact latent space; 2) it establishes full correlation through a tri-stream fusion architecture; 3) it generates occupancy results that strengthen the baseline's output. Extensive experiments demonstrate the efficiency and effectiveness of our method, outperforming the latest baseline models. The source code and several demos are available in the supplementary material.

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