OccLinker: Deflickering Occupancy Networks through Lightweight Spatio-Temporal Correlation

Vision-based occupancy networks (VONs) provide an end-to-end solution for reconstructing 3D environments in autonomous driving. However, existing methods often suffer from temporal inconsistencies, manifesting as flickering effects that compromise visual experience and adversely affect decision-making. While recent approaches have incorporated historical data to mitigate the issue, they often incur high computational costs and introduce noisy information that interferes with object detection. We propose OccLinker, a novel plugin framework designed to seamlessly integrate with existing VONs for boosting performance. Our method employs a three-stage architecture that consolidates historical static and motion cues, correlates them with current features through a Motion-Static Integration (MSI) mechanism, and generates correction occupancy to refine base network predictions. Extensive experiments on two benchmarks demonstrate the efficiency and effectiveness of our method, outperforming the latest baseline models. The source code are available in the supplementary material.

@article{yu2025_2502.15438,
  title={ OccLinker: Deflickering Occupancy Networks through Lightweight Spatio-Temporal Correlation },
  author={ Fengcheng Yu and Haoran Xu and Canming Xia and Ziyang Zong and Guang Tan },
  journal={arXiv preprint arXiv:2502.15438},
  year={ 2025 }
}