Efficient Multi-View Inpaint: Algorithms and Applications

Efficient Multi-View Inpaint: Algorithms and Applications

Overview

Efficient multi-view inpaint refers to methods that fill missing or corrupted regions in multi-view image sets (images of the same scene captured from different viewpoints) while preserving cross-view consistency and 3D structure. Efficiency targets computational cost, memory, and inference speed, enabling practical use in large-scale reconstruction, AR/VR, and film post-production.

Key challenges

• Cross-view consistency: Ensuring completed pixels align across views and with scene geometry.
• Geometry awareness: Handling occlusions, varying viewpoints, and parallax.
• Scalability: Processing many high-resolution views with limited resources.
• Appearance coherence: Maintaining texture, color, and lighting consistency.

Algorithms (efficient approaches)

• Sparse multi-view optimization

  • Use feature matches and sparse geometry (e.g., keypoints, depth priors) to guide inpainting only where needed.
  • Low memory and fast when dense reconstruction is unnecessary.

• Learning-based 2D→3D hybrid methods

  • Per-view inpainting networks combined with view-consistency losses or warping using estimated depth.
  • Efficient because per-image networks are lightweight and consistency enforced via sparse sampling.

• Depth-guided reprojection

  • Reproject pixels from neighboring views using estimated depth maps; fill holes by selecting best reprojections before applying lightweight refinement.
  • Reduces neural synthesis load by leveraging real observed pixels.

• Patch-based multi-view synthesis

  • Extend classic patch-match inpainting across views, prioritizing patches from geometrically consistent regions.
  • Simple, fast, and interpretable for many cases.

• Neural radiance field (NeRF)-aware inpainting

  • Train compact radiance/feature fields that can render missing view content; use efficient representations (sparse voxel grids, hash encodings) for speed.
  • Combines 3D consistency with neural synthesis—tradeoff between accuracy and compute.

• Multi-scale and attention-efficient models

  • Use coarse-to-fine pipelines and sparse attention to limit computation to problem areas.
  • Improves speed while preserving details.

Practical components for efficiency

• Precompute and compress depth/feature maps.
• Prioritize reprojection from nearby views before hallucinating content.
• Use GPU-friendly data structures (sparse grids, hash tables).
• Batch processing and tiling for high-resolution inputs.
• Lightweight loss terms focusing on perceived artifacts rather than pixel-perfect matches.

Applications

• 3D reconstruction & photogrammetry: Fill holes in multi-view photogrammetric outputs to improve mesh/texturing.
• AR/VR & telepresence: Generate consistent backgrounds or remove undesired objects across multiple viewpoints.
• Film and visual effects: Repair damaged frames or remove rigs across multi-view setups.
• Cultural heritage digitization: Restore missing texture across multi-view scans of artifacts.
• Robotics and autonomous driving: Complete occluded scene parts for better planning and perception.

Evaluation metrics

• Cross-view consistency: Warping error between rendered/completed views.
• Perceptual quality: LPIPS, FID for image realism.
• Geometry fidelity: Depth/normal consistency.
• Runtime & memory: Throughput (fps), peak memory use.

Limitations & open problems

• Handling extreme occlusions where no view contains the missing content.
• Reliance on accurate depth—errors propagate to reprojections.
• Scaling to very large scenes with varied lighting.
• Balancing speed versus high-frequency detail synthesis.

Recommended workflow (practical)

1. Estimate depth and camera poses from input views.
2. Reproject high-confidence pixels from neighboring views to fill obvious holes.
3. Apply lightweight, depth-guided neural refinement for texture coherence.
4. Validate cross-view consistency by warping completed results; iterate if needed.
5. Optionally train or fine-tune models on target-domain data for best visual match.