Research map / Detection and segmentation / 3D detection and driving
Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PyrOccNet instead of VPN | Semantic BEV map prediction · Argoverse | Mean IoU (%): 13.9 -> 17.0 | Helped |
| Use PyrOccNet instead of VPN | Semantic BEV map prediction · NuScenes | Mean IoU (%): 17.5 -> 19.1 | Helped |
| Add the dense transformer layer to the baseline (ablation) | PyrOccNet · Argoverse | Mean IoU (%): 13.1 -> 15.1 | Helped |
| Add the transformer pyramid to the dense-transformer model (ablation) | PyrOccNet · Argoverse | Mean IoU (%): 15.1 -> 16.1 | Helped |
| Add the topdown network to the dense-transformer-plus-pyramid model (ablation) | PyrOccNet · Argoverse | Mean IoU (%): 16.1 -> 17.0 | Helped |
| Add the topdown network (ablation) | PyrOccNet · Argoverse drivable area | IoU (%): 65.9 -> 65.4 | Hurt |
Closest papers by meaning
- Achieving Speed-Accuracy Balance in Vision-based 3D Occupancy Prediction via Geometric-Semantic Disentanglement (AAAI 2025 · similarity 0.89)
- UnO: Unsupervised Occupancy Fields for Perception and Forecasting (CVPR 2024 · similarity 0.89)
- CSV-Occ: Fusing Multi-frame Alignment for Occupancy Prediction with Temporal Cross State Space Model and Central Voting Mechanism (ICML 2025 · similarity 0.88)
- EvOcc: Accurate Semantic Occupancy for Automated Driving Using Evidence Theory (CVPR 2025 · similarity 0.88)
- Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory (CVPR 2024 · similarity 0.88)
- Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles (CVPR 2024 · similarity 0.88)
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