Research map / 3D vision / Depth and stereo
Mirror Surface Reconstruction Under an Uncalibrated Camera
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use cross-ratio optimization instead of the linear solution (ablation) | Mirror surface reconstruction · Stanford bunny, σ = 2.0 pixels | focal length fu estimation error: 1.18% -> 0.14% | Helped |
| Use cross-ratio optimization instead of the constrained linear solution (ablation) | Mirror surface reconstruction · Stanford bunny, σ = 2.0 pixels | focal length fu estimation error: 1.32% -> 0.14% | Helped |
| Estimate plane poses rather than using calibrated plane poses (ablation) | Mirror surface reconstruction · Sauce boat | RMS reconstruction error: 2.55 -> 2.71 | Hurt |
| Estimate plane poses rather than using calibrated plane poses (ablation) | Mirror surface reconstruction · Two spheres | RMS reconstruction error: 1.78 -> 2.03 | Hurt |
| Use uncalibrated camera and plane poses instead of calibrated camera and plane poses | Mirror surface reconstruction · Sphere patch | RMS reconstruction error | No clear effect |
Closest papers by meaning
- NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images (CVPR 2024 · similarity 0.84)
- PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction (CVPR 2025 · similarity 0.83)
- UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections (NeurIPS 2024 · similarity 0.83)
- MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification (ICML 2025 · similarity 0.83)
- GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors (ICLR 2024 · similarity 0.82)
- Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction (NeurIPS 2024 · similarity 0.82)
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