Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/TanyaChutani/Monodepth-Tf2.x
https://github.com/Taoudi/LotteryTicketHypothesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ThanasisMattas/smartflow
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/Tom-Zheng/depth_single_image
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/Tools4Project/4501Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/Turmac/DIW_TF_Implementation
https://github.com/UKPLab/coling18-multimodalSurvey
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/UnofficialJuliaMirror/DeepMark-deepmark
https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark
https://github.com/VCL3D/3D60
Spherical View Synthesis for Self-Supervised 360 Depth Estimation
https://github.com/VCL3D/SphericalViewSynthesis
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas
https://github.com/VICO-UoE/KD4MTL
https://github.com/Vakihito/SentimentYoutube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Vamshi399/CarND-Vehicle-Detection
https://github.com/VamsiKrishna1211/gender_age-detection
https://github.com/VhalPurohit/290s
https://github.com/VinayBN8997/ResNet-CIFAR10
https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/Vivswan/ECE-3195-Course-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/WeLoveKiraboshi/In-PlaneRotation-AwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/WeLoveKiraboshi/In-PlaneRotationAwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/XXXVincent/MonoDepth2
https://github.com/Xnsam/clothing_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Yashgh7076/CPSC-8810-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Yc174/monodepth
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/Yeh-yu-hsuan/BiFuse
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YvanYin/DiverseDepth
DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data
https://github.com/YvanYin/VNL_Monocular_Depth_Prediction
Enforcing geometric constraints of virtual normal for depth prediction
https://github.com/ZRandomize/MatrixVT
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZacharyGoshen/cifar-10-classifier
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.