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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 39 papers

TitleStatusHype
Fine-Tuning Image-Conditional Diffusion Models is Easier than You ThinkCode4
iDisc: Internal Discretization for Monocular Depth EstimationCode3
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene UnderstandingCode2
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and DenoisingCode2
BlenderProcCode2
Extracting Triangular 3D Models, Materials, and Lighting From ImagesCode2
AI Playground: Unreal Engine-based Data Ablation Tool for Deep LearningCode1
DeepFit: 3D Surface Fitting via Neural Network Weighted Least SquaresCode1
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal EstimationCode1
AdaFit: Rethinking Learning-based Normal Estimation on Point CloudsCode1
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal EstimationCode1
GRIT: General Robust Image Task BenchmarkCode1
How Well Do Self-Supervised Models Transfer?Code1
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper SurfacesCode1
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry EstimationCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge ConditioningCode1
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect IlluminationCode1
NeAF: Learning Neural Angle Fields for Point Normal EstimationCode1
NeRD: Neural Reflectance Decomposition from Image CollectionsCode1
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown IlluminationCode1
Stanford-ORB: A Real-World 3D Object Inverse Rendering BenchmarkCode1
Robust Learning Through Cross-Task ConsistencyCode1
Robust Learning Through Cross-Task ConsistencyCode1
360^o Surface Regression with a Hyper-Sphere LossCode0
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All datasetsPCPNetStanford-ORBNYU-Depth V2ScanNetV2IBims-1NYU-Depth V2 Surface NormalsPASCAL ContextTaskonomy

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Nesti-NetRMSE 12.41—Unverified
2Iter-NetRMSE 11.84—Unverified
3DeepFitRMSE 11.8—Unverified
4AdaFitRMSE 10.76—Unverified
5GraphFitRMSE 10.26—Unverified
6NeAFRMSE 10.22—Unverified
7HsurfRMSE 10.11—Unverified
8MSECNetRMSE 9.76—Unverified
#ModelMetricClaimedVerifiedStatus
1NeRFactorCosine Distance0.29—Unverified
2NeRDCosine Distance0.28—Unverified
3PhySGCosine Distance0.17—Unverified
4NVDiffRecCosine Distance0.06—Unverified
5Neural-PBIRCosine Distance0.06—Unverified
6InvRenderCosine Distance0.06—Unverified
7NVDiffRecMCCosine Distance0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Metric3Dv2(L, FT)% < 11.2568.8—Unverified
2PolyMaX(ConvNeXt-L)% < 11.2565.66—Unverified
3iDisc% < 11.2563.8—Unverified
4Bae et al.% < 11.2562.2—Unverified
5Marigold + E2E FT(zero-shot)% < 11.2561.4—Unverified
6Floors are Flat% < 11.2559.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Metric3Dv2 (g2, In-domain)% < 11.2577.8—Unverified
2Bae et al.% < 11.2571.1—Unverified
3Floors are Flat% < 11.2550.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Marigold + E2E FT(zero-shot)% < 11.2569.9—Unverified
2Metric3Dv2(g2, ZS)% < 11.2569.7—Unverified
#ModelMetricClaimedVerifiedStatus
1DSNRMSE12.2—Unverified
#ModelMetricClaimedVerifiedStatus
1InvPTMean Angle Error14.15—Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error4.8—Unverified