Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 14
- 보존된 평가 기록
- 58
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/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aasharma90/CycleStereoGAN_NighttimeDepth
https://github.com/abaietto/neural_ode_classification
https://github.com/abefetterman/hamstir-gym
CAD2RL: Real Single-Image Flight without a Single Real Image
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/adldotori/ResNet
https://github.com/adricarda/AT-DT-Pytorch-implementation
https://github.com/agmm/colab-3d-ken-burns
https://github.com/ahbpp/VTDepth
https://github.com/ahmad0790/svhn-multi-digit-address-recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmedbhna/VGG_Paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmedmostafa0x61/Depth_Estimation
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/aimbsg/EVA4_S15
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ajaystar8/PDRUNet-PyTorch
https://github.com/ajithvallabai/getsetgo_keras-beginner
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akamaster/pytorch_resnet_cifar10
https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akhadangi/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akommini/Spoken-Numeric-Digit-detection
https://github.com/akshnataraj/Computer-Vision-literature
https://github.com/alalagong/LEDNet
https://github.com/albert100121/360SD-Net
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
https://github.com/albertomontesg/keras-model-zoo
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alecwangcq/KFAC-Pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alex-f1tor/Image-Caption
https://github.com/alexeyab/midas
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/alexsax/midlevel-reps
https://github.com/alexwdong/IncubatorCVProject
https://github.com/alibaba/EasyCV
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.