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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–10 of 39 papers

Title	Date	Tasks	Status	Hype	Score
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think	Sep 17, 2024	Conditional Image GenerationDepth Estimation	CodeCode Available	4	5
iDisc: Internal Discretization for Monocular Depth Estimation	Apr 13, 2023	Autonomous DrivingDepth Estimation	CodeCode Available	3	5
Extracting Triangular 3D Models, Materials, and Lighting From Images	Nov 24, 2021	Depth PredictionImage Relighting	CodeCode Available	2	5
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding	Mar 15, 2022	Boundary DetectionHuman Parsing	CodeCode Available	2	5
BlenderProc	Oct 25, 2019	3D Object RecognitionDepth Image Estimation	CodeCode Available	2	5
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising	Jun 7, 2022	3D ReconstructionDenoising	CodeCode Available	2	5
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation	Jul 23, 2022	Surface Normals Estimation	CodeCode Available	1	5
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning	Jul 13, 2020	Data AblationDepth Estimation	CodeCode Available	1	5
GRIT: General Robust Image Task Benchmark	Apr 28, 2022	Instance SegmentationKeypoint Detection	CodeCode Available	1	5
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation	Sep 20, 2021	DecoderPrediction	CodeCode Available	1	5

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All datasets PCPNet Stanford-ORB NYU-Depth V2 ScanNetV2 IBims-1 NYU-Depth V2 Surface Normals PASCAL Context Taskonomy

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Nesti-Net	RMSE	12.41	—	Unverified
2	Iter-Net	RMSE	11.84	—	Unverified
3	DeepFit	RMSE	11.8	—	Unverified
4	AdaFit	RMSE	10.76	—	Unverified
5	GraphFit	RMSE	10.26	—	Unverified
6	NeAF	RMSE	10.22	—	Unverified
7	Hsurf	RMSE	10.11	—	Unverified
8	MSECNet	RMSE	9.76	—	Unverified

#	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	InvRender	Cosine Distance	0.06	—	Unverified
5	NVDiffRec	Cosine Distance	0.06	—	Unverified
6	Neural-PBIR	Cosine Distance	0.06	—	Unverified
7	NVDiffRecMC	Cosine Distance	0.04	—	Unverified

#	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

#	Model	Metric	Claimed	Verified	Status
1	Metric3Dv2 (g2, In-domain)	% < 11.25	77.8	—	Unverified
2	Bae et al.	% < 11.25	71.1	—	Unverified
3	Floors are Flat	% < 11.25	50.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Marigold + E2E FT(zero-shot)	% < 11.25	69.9	—	Unverified
2	Metric3Dv2(g2, ZS)	% < 11.25	69.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DSN	RMSE	12.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InvPT	Mean Angle Error	14.15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	X-TC (Cross-Task Consistency)	L1 error	4.8	—	Unverified