SOTAVerified

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

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Showing 1–10 of 39 papers

TitleStatusHype
Learning from the Giants: A Practical Approach to Underwater Depth and Surface Normals Estimation—0
Fine-Tuning Image-Conditional Diffusion Models is Easier than You ThinkCode4
PolyMaX: General Dense Prediction with Mask TransformerCode0
Stanford-ORB: A Real-World 3D Object Inverse Rendering BenchmarkCode1
Large-scale Monocular Depth Estimation in the Wild—0
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge ConditioningCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
Neural-PBIR Reconstruction of Shape, Material, and Illumination—0
iDisc: Internal Discretization for Monocular Depth EstimationCode3
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect IlluminationCode1
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All datasetsPCPNetStanford-ORBNYU-Depth V2ScanNetV2IBims-1NYU-Depth V2 Surface NormalsPASCAL ContextTaskonomy

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Metric3Dv2 (g2, In-domain)% < 11.2577.8—Unverified
2Bae et al.% < 11.2571.1—Unverified
3Floors are Flat% < 11.2550.9—Unverified