  3D occupancy prediction based on multi-sensor fusion,crucial for a reliable
autonomous driving system, enables fine-grained understanding of 3D scenes.
Previous fusion-based 3D occupancy predictions relied on depth estimation for
processing 2D image features. However, depth estimation is an ill-posed
problem, hindering the accuracy and robustness of these methods. Furthermore,
fine-grained occupancy prediction demands extensive computational resources. To
address these issues, we propose OccFusion, a depth estimation free multi-modal
fusion framework. Additionally, we introduce a generalizable active training
method and an active decoder that can be applied to any occupancy prediction
model, with the potential to enhance their performance. Experiments conducted
on nuScenes-Occupancy and nuScenes-Occ3D demonstrate our framework's superior
performance. Detailed ablation studies highlight the effectiveness of each
proposed method.
