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Surface Normals Estimation
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation
PolyMaX: General Dense Prediction with Mask Transformer
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
MIMIC: Masked Image Modeling with Image Correspondences
Neural-PBIR Reconstruction of Shape, Material, and Illumination
iDisc: Internal Discretization for Monocular Depth Estimation
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
NeAF: Learning Neural Angle Fields for Point Normal Estimation
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
GRIT: General Robust Image Task Benchmark
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Extracting Triangular 3D Models, Materials, and Lighting From Images
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
NeRD: Neural Reflectance Decomposition from Image Collections
How Well Do Self-Supervised Models Transfer?
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
Robust Learning Through Cross-Task Consistency
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
BlenderProc
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
$360^o$ Surface Regression with a Hyper-Sphere Loss
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
Deep Iterative Surface Normal Estimation
Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
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