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.
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https://github.com/Candice-X/w-net-for-image-segmentation
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/CaptainEven/MonoDepthV2
https://github.com/CarloRadice/depth-and-motion-learning
https://github.com/CellSMB/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/ChelseaGH/sidewalk_prototype_AI_Hub
https://github.com/ClementPinard/SfmLearner-Pytorch
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/CodeChefVIT/VOID
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/DaikiTanak/manifold_mixup
https://github.com/DeadAt0m/DCA-PyTorch
Deep Component Analysis via Alternating Direction Neural Networks
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Deeksha-Pandit/EVA4-S10
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/DeepMIALab/VirtualCapsuleEndoscopy
https://github.com/DeepMark/deepmark
https://github.com/DepthAnything/Video-Depth-Anything
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
https://github.com/Di-Is/stylegan2-ada-pytorch
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ENSTA-U2IS/MUAD-Dataset
https://github.com/EPFL-VILAB/3DCommonCorruptions
https://github.com/EbadSyed/spadRGBD
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EngineeringSapien/NarutoCV
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.