Core Advantage:
Multi-camera arrays (e.g., 8+ synchronized cameras) provide high-resolution, wide-baseline 3D capture, enabling dense light field reconstruction or depth fusion for high-quality autostereoscopy.
Key Capture Metrics & Methods
1. Camera Alignment & Synchronization
- Geometric Calibration:
- Extrinsic (mm-level camera position accuracy) + intrinsic (lens distortion <0.1px).
- Tool: Checkerboard patterns + bundle adjustment (e.g., COLMAP).
- Temporal Sync:
- Hardware triggers or genlock to minimize inter-frame delay (<100µs).
2. Depth/View Synthesis Quality
- Stereo Matching:
- MVS (Multi-View Stereo) algorithms (e.g., PatchMatch) generate depth maps.
- Metric: Depth error (RMSE) vs. ground truth (e.g., LiDAR).
- Light Field Interpolation:
- Angular super-resolution (e.g., CNN-based view synthesis).
3. Parallax Range & Continuity
- Baseline Optimization:
- Wider spacing = stronger depth but risks occlusion holes.
- Metric: Occlusion coverage (%) after inpainting.
Optimization Techniques
- Hybrid Arrays: Combine wide/narrow baselines (e.g., 4× wide + 4× narrow).
- Real-Time Preprocessing: FPGA-based rectification/streaming.
- Neural Radiance Fields (NeRF): For novel-view synthesis from sparse inputs.
Validation Setup
- Test Scenes:
- Dynamic objects (evaluate temporal consistency).
- Fine textures (test stereo matching limits).
- Tools:
- OpenMVS, NVIDIA Omniverse for reconstruction.
