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LidarBoost : Depth Superresolution for ToF 3D Shape Scanning

LidarBoost : Depth Superresolution for ToF 3D Shape Scanning. CVPR’09 Reporter ： Jheng -You Lin. Outlin e. Introduction Algorithm Results. Introduction. MRF based resolution enhancement Image superresolution Solve an energy minimization problem. . Algorithm. Algorithm data term.

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LidarBoost : Depth Superresolution for ToF 3D Shape Scanning

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  1. LidarBoost: Depth Superresolution for ToF 3D Shape Scanning CVPR’09 Reporter：Jheng-You Lin

  2. Outline • Introduction • Algorithm • Results

  3. Introduction • MRF based resolution enhancement • Image superresolution • Solve an energy minimization problem.

  4. Algorithm

  5. Algorithm data term • Interpolated sampling → nearest neighbor sampling • 1-norm → 2-norm

  6. Algorithm regularterm • Key point : high frequency 3D shape features, suppress noise. • sum-of-gradient-norms → sum-of-norms IBSR

  7. Algorithm

  8. Results Synthetic Scene - No Noise Added N = 10, Input depth maps come from 400x400 ground truth depth maps, and downsampled by factor 8.

  9. Results Synthetic Scene - Stark Noise Added

  10. Results Synthetic Scene - Quantitative Comparison rMSE error

  11. Results Synthetic Scene - Collection of Objects

  12. Results Synthetic Scene - Wedges and Panels

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