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 | NeRFactor | Cosine Distance | 0.29 | — | Unverified |
| 2 | NeRD | Cosine Distance | 0.28 | — | Unverified |
| 3 | PhySG | Cosine Distance | 0.17 | — | Unverified |
| 4 | Neural-PBIR | Cosine Distance | 0.06 | — | Unverified |
| 5 | NVDiffRec | Cosine Distance | 0.06 | — | Unverified |
| 6 | InvRender | Cosine Distance | 0.06 | — | Unverified |
| 7 | NVDiffRecMC | Cosine Distance | 0.04 | — | Unverified |