Saturation-Guided Temporal Context Allocation Before 3D Occupancy Prediction

Scene-adaptive temporal context allocation enables 3D occupancy predictors to tailor the history length to each scene. Existing adaptive methods infer this demand from detections, predicted observability, learned features, or other predictor-generated signals. Coupling the selection rule to a particular perception architecture limits its direct transfer to other occupancy predictors, even on the same dataset. We ask whether the required history can instead be determined directly from raw camera–LiDAR observations. Our key observation is that the additional geometric coverage and visual diversity contributed by older frames saturate at scene-dependent rates. Based on this observation, we propose Composite Scene Entropy (CSE), a predictor-external proxy that quantifies this saturation over nested temporal windows. CSE combines global point-cloud entropy, local point-cloud entropy, and image-gradient entropy to characterize global geometric coverage, local structure, and visual diversity, respectively. We select the shortest window whose marginal CSE gain has saturated and feed it to an otherwise unchanged occupancy predictor. Extensive experiments on nuScenes-SurroundOcc show that one CSE calibration and its per-sample allocations can be reused across three compatible occupancy predictors; with SAGFormer, CSE improves Fixed-9 by 1.44 mIoU at the same average window length and nearly matches Fixed-16 with 52.8% lower model-side latency. On SSCBench-KITTI-360, an independent calibration yields the same CSE parameter values as on nuScenes, and CSE allocation remains within 0.03 mIoU of Fixed-16.