This writeup summarizes practical engineering choices for building reliable camera-LiDAR fusion stacks beyond benchmark-only performance.
Fusion models often degrade under calibration drift, weather-induced LiDAR sparsity, and camera motion blur.
Use explicit calibration validation, confidence-aware modality weighting, and fault-tolerant fallback logic for sensor degradation.
Teams can improve deployment stability by prioritizing calibration health checks and condition-aware fusion policies.