Research map / Learning theory and algorithms / Combinatorial and search optimization
Optimal Structured Light à La Carte
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize code matrices for the specified imaging conditions and constraints | Structured-light triangulation · Quantitative evaluations in Figure 7 | fraction of correct correspondences | Helped |
| Add geometry constraints to code optimization and decoding (ablation) | Structured-light codes · Low pattern counts | fraction of correct correspondences | Helped |
| Add geometry constraints to code optimization (ablation) | Optimized codes · Low pattern counts | fraction of correct correspondences | Helped |
| Increase the maximum spatial frequency of the patterns (ablation) | Optimized codes, MPS, and EPS · Quantitative evaluation across spatial frequencies | fraction of correct correspondences | Mixed |
| Use geometry-constrained codes optimized for a small correspondence-error tolerance (ablation) | Structured-light codes · Quantitative evaluation across spatial frequencies | RMSE | Helped |
| Adapt code matrices to lower peak signal-to-noise ratio conditions (ablation) | Optimized codes · Real-world performance | reconstruction performance | No clear effect |
| Use a camera-specific noise model when optimizing codes (ablation) | Position encoding · Real-world performance | reconstruction performance | Helped |
| Use ZNCC decoding instead of pattern-specific native decoders (ablation) | MPS, Gray, and XOR codes · Planar-target comparison in Figure 5 | correspondence accuracy | No clear effect |
| Increase the number of projection patterns (ablation) | Optimized codes compared with MPS and EPS · Quantitative evaluation | performance gap over baselines | Hurt |
| Replace exact correspondence optimization with a nonzero tolerance (ablation) | Optimized code matrices · Code-matrix structure in Figure 6 | pairwise code-vector similarity structure | Mixed |
Closest papers by meaning
- Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo (CVPR 2021 · similarity 0.89)
- TurboSL: Dense Accurate and Fast 3D by Neural Inverse Structured Light (CVPR 2024 · similarity 0.88)
- Information-Driven Adaptive Structured-Light Scanners (CVPR 2016 · similarity 0.88)
- A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance (CVPR 2023 · similarity 0.87)
- Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic Scenes (CVPR 2025 · similarity 0.84)
- Sparse Views Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo (CVPR 2024 · similarity 0.84)
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