LMPOcc: 3D Semantic Occupancy Prediction Utilizing Long-Term Memory Prior from Historical Traversals

Shanshuai Yuan1, Julong Wei1, Muer Tie1, Xiangyun Ren2, Zhongxue Gan1, Wenchao Ding1
1Fudan University, 2Chongqing Changan Automobile CO., Ltd
Paper Code arXiv

Abstract

Vision-based 3D semantic occupancy prediction iscritical for autonomous driving, enabling unified modeling of static infrastructure and dynamic agents. In practice, autonomous vehicles may repeatedly traverse identical geographic locations under varying environmental conditions, such as weather fluctuations and illumination changes. Existing methods in 3D occupancy prediction predominantly integrate adjacent temporal contexts. However, these works neglect to leverage perceptual information, which is acquired from historical traversals of identical geographic locations. In this paper, we propose Longterm Memory Prior Occupancy (LMPOcc), the first 3D occupancy prediction methodology that exploits long-term memory priors derived from historical traversal perceptual outputs. We introduce a plug-and-play architecture that integrates long-term memory priors to enhance local perception while simultaneously constructing global occupancy representations. To adaptively aggregate prior features and current features, we develop an efficient lightweight Current-Prior Fusion module. Moreover, we propose a model-agnostic prior format to ensure compatibility across diverse occupancy prediction baselines. LMPOcc achieves state-of-the-art performance validated on the Occ3D-nuScenes benchmark, especially on static semantic categories. Additionally, experimental results demonstrate LMPOcc’s ability to construct global occupancy through multi-vehicle crowdsourcing.

Framework

Framework architecture diagram

LMPOcc fristly generates Current Latent Features from surround-view images. Then it extracts spatiallyaligned Prior Features from global occupancy and integrates them via the Current-Prior Fusion Module to generate Refined Latent Features. The refined latent features decode current occupancy logits, which are stored into corresponding locations in the global occupancy after visibility masking. Existing occupancy priors at these locations are replaced by the updated logits. Finally, the occupancy logits are converted into local current occupancy prediction results.

Visualization

Framework architecture diagram

The left, center, and right columns demonstrate the performance under sunny, rainy, and night-time conditions respectively.

Visualization results of global occupancy construction via crowdsourcing methodologies. Three collaborative agents construct the global occupancy map through crowdsourcing.

Quantitative Experiments

Framework architecture diagram

3D occupancy prediction performance on the Occ3D-nuScenes validation set. Both the small version and large version of LMPOcc outperform the models that have similar settings.

Framework architecture diagram

The performance of occupancy prediction methods and their LMOP versions on the Occ3D-nuScenes validation set. By adding long-term memory knowledge, LMOP consistently improves these methods.

Video

BibTeX


        @article{yuan2025lmpocc,
          title={LMPOcc: 3D Semantic Occupancy Prediction Utilizing Long-Term Memory Prior from Historical Traversals},
          author={Yuan, Shanshuai and Wei, Julong and Tie, Muer and Ren, Xiangyun and Gan, Zhongxue and Ding, Wenchao},
          journal={arXiv preprint arXiv:2504.13596},
          year={2025}
        }