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[BEV Projection] Forward and Backward Projection
BEV Projection
Forward Projection vs Backward Projection
Comparison of Projection Methods in BEV Perception
| Aspect | Forward Projection (e.g., LSS, BEVDepth) | Backward/Query-Based Projection (e.g., BEVFormer) |
|---|---|---|
| Memory Cost | Smaller(depth estimation module) | Larger(Deformable CrossAttn+Temporal SelfAttn+FNN) |
| Data Scalability | Easy: Can be pre-trained with any camera data. | Hard: Requires large datasets with 3D infos(3D Labels,3D Egomotion) |
| Explanability | Strong | Weak(maybe can check sampling points) |
| BEV Density | Sparse | Dense |
| Calibration Error | Sensitive | Robust |
| Depth Scale Umbiguity | Sensitive | Robust |
| Image Discretization | Discreet. Forward Project Discreet Feature Map | Continuous: Backward Project to Continuous Image by bilinear sample |
| Iterative Refinement | No | Yes |
| Parallelization | Harder: Random Write(Atomic) | Easy: Random Read |
| Occlusion | Struggles Difficult to know occluded or empty voxel grid | Better: Can be learned whether occluded or empty voxel |
| Temporal Modeling | Limited | Sophisticated: Temporal Self Attention |
Reference
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
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