Papers with Code 아카이브
Surface Normals Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DrSleep/multi-task-refinenet
공식 코드Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
https://github.com/EPFL-VILAB/XTConsistency
공식 코드https://github.com/FuxiComputerVision/Nefii
공식 코드NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
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공식 코드Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
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공식 코드ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
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공식 코드Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
https://github.com/StevenHickson/CreateNormals
공식 코드Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
https://github.com/SysCV/idisc
공식 코드iDisc: Internal Discretization for Monocular Depth Estimation
https://github.com/VisualComputingInstitute/diffusion-e2e-ft
공식 코드Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
https://github.com/allenai/grit_official
공식 코드https://github.com/baegwangbin/surface_normal_uncertainty
공식 코드Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
https://github.com/cgtuebingen/nerd-neural-reflectance-decomposition
공식 코드NeRD: Neural Reflectance Decomposition from Image Collections
https://github.com/google-research/deeplab2
공식 코드https://github.com/google/nerfactor
공식 코드NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
https://github.com/koutilya40192/SharinGAN
공식 코드SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
https://github.com/leoqli/hsurf-net
공식 코드HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
https://github.com/leoshine/Spherical_Regression
공식 코드Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres
https://github.com/linusericsson/ssl-transfer
공식 코드https://github.com/lisj575/NeAF
공식 코드NeAF: Learning Neural Angle Fields for Point Normal Estimation
https://github.com/martianxiu/MSECNet
공식 코드MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
https://github.com/nnaisense/deep-iterative-surface-normal-estimation
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https://github.com/raivnlab/mimic
공식 코드https://github.com/runsong123/adafit
공식 코드AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
https://github.com/sitzikbs/DeepFit
공식 코드DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
https://github.com/sitzikbs/Nesti-Net
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공식 코드GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
https://github.com/yvanyin/metric3d
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Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
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https://github.com/MindSpore-paper-code-2/code2/tree/main/RefineNet
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.