OCC-Exoskeleton: A Plug-and-Play Module to Enhance CNN-Based Occupancy Prediction Networks

In 3D semantic occupancy prediction, both the task-specific characteristics and input data critically influence network perception performance. It encounters many challenges, such as data-label misalignment, spatial-significance variance, and long-tailed distribution in semantic occupancy prediction labels. To deal with these challenges, we propose a generalized auxiliary enhancement module, termed OCC-Exoskeleton, for semantic occupancy prediction. The proposed module demonstrates remarkable adaptability, enabling seamless integration with diverse occupancy prediction models while maintaining architectural compatibility. Our module is made up of three parts, each of which is specifically designed to address one of the three mentioned challenges: 1) Virtual point cloud distillation. We generate the virtual point cloud, teaching realistic modalities to concentrate on the data-label misalignment positions. 2) Dual-expert occupancy head. We allocate the occupancy prediction task to two expert heads according to spatial significance to obtain more targeted outcomes. 3) Scene-level frames mixture augmentation. We propose a frames mixture augmentation method that introduces additional foreground objects to create more complex driving scenes, alleviating the long-tailed distribution and enhancing the model’s robustness. Furthermore, the proposed module functions as an efficient plug-and-play module, capable of enhancing the performance of existing network architectures while maintaining minimal computational overhead. Extensive experiments demonstrate that our module achieves significant performance improvement in a range of methods with different input modalities.