Objective:
Accurate real-world scene digitization for autostereoscopic displays requires high-fidelity depth + RGB fusion to enable glasses-free 3D viewing.
Key Components & Metrics:
- Depth Sensor Selection:
- LiDAR/ToF: Measures depth via time-of-flight (sub-cm precision, struggles with reflectivity).
- Stereo RGB: Passive depth from dual cameras (lower cost, sensitive to lighting).
- Metric: Depth error (mm) vs. ground truth (e.g., structured light scan).
- RGB-D Alignment:
- Temporal Sync: Ensures depth/RGB frames are captured simultaneously (<1ms skew).
- Spatial Calibration: Corrects parallax errors between sensors (reprojection error <0.5px).
- Fusion Algorithms:
- Point Cloud Registration: ICP or neural networks (e.g., FlowNet3D) merge multi-sensor data.
- Metric: Hole-filling rate (%), edge preservation (PSNR).
Challenges:
- Dynamic Scenes: Motion artifacts from sensor latency.
- Transparent/Reflective Surfaces: Depth sensor inaccuracies.
Optimization:
- Hybrid Sensing: Combine ToF + stereo RGB for robustness.
- AI-Based Denoising: DL models (e.g., 3D CNNs) clean raw depth maps.
Validation Setup:
- Test Patterns: Checkersboards, depth staircases.
- Ground Truth: High-precision 3D scanners (e.g., photogrammetry).
