Temporal Triplane Transformers as Occupancy World Models

Recent years have seen significant advances in world models, which primarily focus on learning fine-grained correlations between an agent's motion trajectory and the resulting changes in its surrounding environment. However, existing methods often struggle to capture such fine-grained correlations and achieve real-time predictions. To address this, we propose a new 4D occupancy world model for autonomous driving, termed T$^3$Former. T$^3$Former begins by pre-training a compact triplane representation that efficiently compresses the 3D semantically occupied environment. Next, T$^3$Former extracts multi-scale temporal motion features from the historical triplane and employs an autoregressive approach to iteratively predict the next triplane changes. Finally, T$^3$Former combines the triplane changes with the previous ones to decode them into future occupancy results and ego-motion trajectories. Experimental results demonstrate the superiority of T$^3$Former, achieving 1.44$\times$ faster inference speed (26 FPS), while improving the mean IoU to 36.09 and reducing the mean absolute planning error to 1.0 meters.