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DIO: Decomposable Implicit 4D Occupancy-Flow World Model
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Proposes DIO, a prompt-driven implicit 4D occupancy-flow world model trained on LiDAR plus bounding-box supervision that decomposes scene occupancy into instances and forecasts them, evaluated on Argoverse 2.
Problem
Existing 4D occupancy world models cannot decompose dynamic scenes into individual instances, struggle with fine-grained structure, and forecast poorly; instance-based and BEV methods have their own bottlenecks.
Approach
A decoder conditioned on a source point (instance prompt, from a detector or label centroid, or empty for scene) and a 4D query point predicts occupancy and flow, enabling decomposed instance occupancy from a sparse 3D backbone with deformable attention, trained with LiDAR ray self-supervision plus box-based instance prompts and a flow consistency loss.
Key results
- On vehicle 4D occupancy, DIO-D reaches 94.36 instance-level AP at completion and 86.01 mAP_I when forecasting, versus 82.99 for UnO.
- On the Argoverse 2 LiDAR forecasting leaderboard, DIO-∅ achieves the best L1 (2.11 m) versus UnO (2.24 m).
- On held-out classes (Bicyclist, Stopsign, Dog), DIO-∅ reaches 95.53 vs 89.43 mAP for UnO-level comparisons on the vehicle class, showing open-set generalization.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use DIO with detector-centroid source prompts instead of UnO | DIO-D · Argoverse 2 vehicle occupancy completion | mAPI: 89.43 -> 94.36 | Helped |
| use DIO with detector-centroid source prompts instead of UnO | DIO-D · Argoverse 2 vehicle occupancy forecasting | mAPI: 82.99 -> 86.01 | Helped |
| use DIO occupancy as the LiDAR forecasting model instead of UnO | DIO-∅ · Argoverse 2 LiDAR forecasting leaderboard | L1 (m): 2.24 -> 2.11 | Helped |
| use DIO-NL instead of ImplicitO-4D | DIO-NL · Argoverse 2 object LiDAR completion | L1 (m): 3.36 -> 0.79 | Helped |
| use DIO-NL instead of ImplicitO-4D | DIO-NL · Argoverse 2 object LiDAR forecasting | L1 (m): 4.94 -> 1.13 | Helped |
| use an empty source point rather than detector centroids (ablation) | DIO · Argoverse 2 vehicle occupancy completion | mAPI: 94.36 -> 95.53 | Helped |
| use label centroids rather than detector centroids (ablation) | DIO · Argoverse 2 vehicle occupancy completion | mAPI: 94.36 -> 95.23 | Helped |
| use noisy label centroids rather than detector centroids (ablation) | DIO · Argoverse 2 vehicle occupancy completion | mAPI: 94.36 -> 95.19 | Helped |
| use label centroids rather than detector centroids (ablation) | DIO · Argoverse 2 vehicle occupancy forecasting | mAPI: 86.01 -> 87.10 | Helped |
| use noisy label centroids rather than detector centroids (ablation) | DIO · Argoverse 2 vehicle occupancy forecasting | mAPI: 86.01 -> 87.04 | Helped |
| use empty-source scene prompts rather than noisy label-centroid prompts (ablation) | DIO · Argoverse 2 held-out-class occupancy completion | mAPI: 97.61 -> 98.43 | Helped |
| use noisy label-centroid prompts rather than empty-source prompts (ablation) | DIO · Argoverse 2 held-out-class occupancy forecasting | mAPI: 88.45 -> 82.95 | Hurt |
Surprising findings
- Using an imperfect off-the-shelf detector (HED-Net) for source points outperforms end-to-end trained baselines.
- Prompting with an empty source point gives the best shape completion on unseen classes, since no semantics are needed.
Limitations
- Still requires bounding box labels during training.
- Instance prompts depend on source points from a detector or labels.
Experimental setup
Datasets: Argoverse 2 Sensor dataset
Benchmarks: Argoverse 2 Sensor dataset Argoverse 4D occupancy forecasting leaderboard
Metrics: mAP Soft-IoU L1 AbsRel NFCD Chamfer Distance HitRate
Baselines: Box Oracle 4D-Occ ImplicitO-4D UnO RayTracing
Builds on: UnO ImplicitO-4D 4D-Occ SECOND Segment Anything
Closest papers by meaning
- Towards foundational LiDAR world models with efficient latent flow matching (NeurIPS 2025 · similarity 0.90)
- UnO: Unsupervised Occupancy Fields for Perception and Forecasting (CVPR 2024 · similarity 0.89)
- SDGOCC: Semantic and Depth-Guided Bird's-Eye View Transformation for 3D Multimodal Occupancy Prediction (CVPR 2025 · similarity 0.87)
- Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications (CVPR 2024 · similarity 0.87)
- Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting (CVPR 2025 · similarity 0.86)
- Learning Occupancy for Monocular 3D Object Detection (CVPR 2024 · similarity 0.86)
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