https://github.com/imran3180/depth-map-prediction
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Papers with Code 아카이브
Depth Estimation
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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/interestingzhuo/foodretreival
https://github.com/inyong37/Vision
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/iodncookie/my_loss_monodepth_master
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/ipeter50/ken-burns-effect
https://github.com/isaacschaal/SG_training
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/isennkubilay/monodepth2_tf
https://github.com/ishanchadha01/Photometric-Reconstruction-Endoscopy
Photometric single-view dense 3D reconstruction in endoscopy
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/isl-org/multiobjectiveoptimization
https://github.com/itijyou/ademxapp
https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/j3soon/arxiv-utils
https://github.com/jahaniam/semidepth
Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameswang287/Car-Detection
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/janisgp/Sampling-free-Epistemic-Uncertainty
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/jday96314/Kuzushiji
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiao0805/bts4
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
https://github.com/jiaxinxie97/Video-depth-estimation
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jiweibo/imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jm199504/VGG-NET
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jmfacil/camconvs
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
https://github.com/joeynavarro/facial_expression_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/johngear/eecs504
https://github.com/johnnylu305/resnet-50-101-152
https://github.com/jojo13572001/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/jonbarron/robust_loss_pytorch
https://github.com/jooyae/NVIDIA_STYLEGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jordan-bird/synthetic-fruit-image-generator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtcass01/SIMON
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.