LMPOcc: 3D Semantic Occupancy Prediction Utilizing Long-Term Memory Prior from Historical Traversals
This addresses the challenge of robust perception under varying environmental conditions for autonomous vehicles, representing an incremental advance by integrating historical data into existing methods.
The paper tackles the problem of 3D semantic occupancy prediction for autonomous driving by leveraging long-term memory priors from historical traversals, achieving state-of-the-art performance on the Occ3D-nuScenes benchmark with notable improvements in static semantic categories.
Vision-based 3D semantic occupancy prediction is critical 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.