Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/swathic5/Depth-estimation-using-neural-networks
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
https://github.com/swetak20/Deep_into_CNNs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/t0930198/backup
https://github.com/taewankim1/uncertainty_deeplearning
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/taniokah/liarr2017
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tanjeffreyz/deep-residual-learning
https://github.com/tanyanair/segmentation_uncertainty
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/tarashakhurana/detecting-invisible-people
https://github.com/tcheung99/ResNet_MiniProject
https://github.com/tejaskhot/unsup_mvs
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
https://github.com/tensorflow/models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/audioset
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/slim
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/struct2depth
https://github.com/tensorpack/tensorpack
https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet
https://github.com/the-super-toys/glimpse-models
https://github.com/thiagorjes/depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/thughost2/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/thygate/stable-diffusion-webui-depthmap-script
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
https://github.com/tiagoCuervo/JapaNet
https://github.com/tjqansthd/LapDepth-release
https://github.com/torchjd/torchjd
https://github.com/towhee-io/towhee
https://github.com/townim-faisal/Revisiting-Pixel-Wise-Supervision-for-Face-Anti-Spoofing
https://github.com/trachpro/arcface-tf2
https://github.com/tri-ml/vedet
https://github.com/trungmanhhuynh/Object-Detection-Localization
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
https://github.com/trzy/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tsunghan-mama/Depth-Completion
Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/tub-rip/es-ptam
https://github.com/tungdt92/GFA-CNN
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.