Surface Normals Estimation
33 papers with code • 8 benchmarks • 12 datasets
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.
Benchmarks
These leaderboards are used to track progress in Surface Normals Estimation
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Datasets
ScanNet
NYUv2
PASCAL Context
Taskonomy
IBims-1
GRIT
Stanford-ORB
3D Ken Burns Dataset
Pano3D
SuperCaustics
Latest papers
GRIT: General Robust Image Task Benchmark
Computer vision models excel at making predictions when the test distribution closely resembles the training distribution.
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction.
Extracting Triangular 3D Models, Materials, and Lighting From Images
nvlabs/tiny-cuda-nn
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CVPR 2022
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
baegwangbin/surface_normal_uncertainty
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ICCV 2021
Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
runsong123/adafit
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ICCV 2021
Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points.
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
google/nerfactor
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This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.
NeRD: Neural Reflectance Decomposition from Image Collections
cgtuebingen/nerd-neural-reflectance-decomposition
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ICCV 2021
This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.
How Well Do Self-Supervised Models Transfer?
linusericsson/ssl-transfer
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CVPR 2021
We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction.
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
With AIP, it is trivial to capture the same image under different conditions (e. g., fidelity, lighting, etc.)
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
