Surface Normals Estimation
32 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
Most implemented papers
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
sitzikbs/DeepFit
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ECCV 2020
We propose a surface fitting method for unstructured 3D point clouds.
Robust Learning Through Cross-Task Consistency
EPFL-VILAB/XTConsistency
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CVPR 2020
Visual perception entails solving a wide set of tasks (e. g., object detection, depth estimation, etc).
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
koutilya40192/SharinGAN
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CVPR 2020
Ideally, this results in images from two domains that present shared information to the primary network.
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.)
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.
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.
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.
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.
