https://github.com/EnriqueSolarte/GC-Net-tensorflow
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EricAlcaide/MiniFold
https://github.com/EstherBear/implementation-of-pruning-filters
https://github.com/EverLookNeverSee/diag2model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EvgenyVendrov/DeepLearningCourse
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SWAG
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Android
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux
https://github.com/FangGet/tf-monodepth2
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/GKalliatakis/Keras-VGG16-places365
https://github.com/GabrieleMonte/CoMBInE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Glp91/fire-detection
https://github.com/Glutamat42/epistemic_ensemble_toy_dataset
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/Godot-yry/VGG16_mindspore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gojay001/toolkit-DeepLearning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Goubeast/Focal-WNet
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/GreatV/PP-GuideNet
https://github.com/HHTseng/video-classification
https://github.com/HKBU-HPML/FADNet
https://github.com/HKBU-HPML/IRS
https://github.com/HKUST-Aerial-Robotics/MVDepthNet
MVDepthNet: Real-time Multiview Depth Estimation Neural Network
https://github.com/Halesu/4th-ML100Days
https://github.com/HanxunH/XTransferBench
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/HighCWu/stylegan2-paddle
https://github.com/IBM/NeuronAlignment
https://github.com/IMvision12/keras-vision-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/IVRL/MulT
https://github.com/IcarusWizard/monodepth-paddle
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/IcarusWizard/monodepth2-paddle
https://github.com/Intoxillectual/Monocular-Depth-Estimation-using-DenseNet169
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/JContro/pytorch_projects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.