Surface Normals Estimation
Surface normal estimation deals with the task of predicting the surface orientation of the objects present inside a scene. Refer to Designing Deep Networks for Surface Normal Estimation (Wang et al.) to get a good overview of several design choices that led to the development of a CNN-based surface normal estimator.
Papers
Showing 1–10 of 39 papers
All datasetsPCPNetStanford-ORBNYU-Depth V2ScanNetV2IBims-1NYU-Depth V2 Surface NormalsPASCAL ContextTaskonomy
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Metric3Dv2(L, FT) | % < 11.25 | 68.8 | — | Unverified |
| 2 | PolyMaX(ConvNeXt-L) | % < 11.25 | 65.66 | — | Unverified |
| 3 | iDisc | % < 11.25 | 63.8 | — | Unverified |
| 4 | Bae et al. | % < 11.25 | 62.2 | — | Unverified |
| 5 | Marigold + E2E FT(zero-shot) | % < 11.25 | 61.4 | — | Unverified |
| 6 | Floors are Flat | % < 11.25 | 59.5 | — | Unverified |