Geometry-Aware Spatio-Temporal Context Modeling for 4D Occupancy Forecasting
4D occupancy forecasting models the spatio-temporal evolution of 3D scenes and is crucial for autonomous driving, especially for corner-case simulation. Existing methods often rely on discrete tokenization followed by autoregressive prediction, yet struggle with geometric distortion in static structures and inconsistent temporal coherence over the forecasting horizon. In this work, we propose a Geometry-Aware Spatio-Temporal context modeling method (GAST) for 4D occupancy forecasting, built upon progressive explicit-implicit generation and dual-path spatio-temporal modeling. Specifically, the generation module produces per-frame occupancy with high geometric fidelity and semantic plausibi...
| Authors | Sitao Chen, Zhuangwei Zhuang, Hui Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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