Research map / 3D vision / Face and shape
Two-Shot Spatially-Varying BRDF and Shape Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use guided stage-wise cascaded estimation with joint refinement instead of a single joint network (ablation) | Two-Shot Spatially-Varying BRDF and Shape Estimation · 20 unseen synthetic objects | Normal MSE: 0.025 -> 0.021 | Helped |
| Use MergeConv blocks rather than standard convolutions for shape estimation (ablation) | Shape estimation network · Synthetic shape estimation | Depth MSE: 0.021 to 0.016 | Helped |
| Use MergeConv blocks rather than standard convolutions for shape estimation (ablation) | Shape estimation network · Synthetic shape estimation | Normal MSE: 0.026 to 0.021 | Helped |
| Use two-shot flash and no-flash inputs instead of a single flash image | SVBRDF and shape estimation · Real-world two-shot images | Visual reconstruction quality | Helped |
Closest papers by meaning
- SVBRDF-Invariant Shape and Reflectance Estimation From Light-Field Cameras (CVPR 2016 · similarity 0.88)
- On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner (CVPR 2020 · similarity 0.87)
- CRISP: Object Pose and Shape Estimation with Test-Time Adaptation (CVPR 2025 · similarity 0.83)
- SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild (CVPR 2024 · similarity 0.83)
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- BLURD: Benchmarking and Learning using a Unified Rendering and Diffusion Model (NeurIPS 2024 · similarity 0.82)
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