Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/shtnkgm/VisionFrameworkSample
https://github.com/shubham0204/Realtime_MiDaS_Depth_Estimation_Android
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/shuuchen/depth_eigen
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/sicxu/Deep3DFaceRecon_pytorch
https://github.com/siddharthbhonge/Neural_Style_transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/siddinc/monocular_depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/sidify/resnet_focal_loss
https://github.com/simo-bat/Crack_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/singh-shakti94/Deep-Learning-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sitegui/ceci-nest-pas-un-chat
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sjsu-smart-lab/Self-supervised-Monocular-Trained-Depth-Estimation-using-Self-attention-and-Discrete-Disparity-Volum
Deep Ordinal Regression Network for Monocular Depth Estimation
https://github.com/skkuse02/2019fall_41class_team8
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/skumailraza/FRSNet-GA
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
https://github.com/smc-x/ms-resnetv2
https://github.com/smhall97/deep_dreaming_music
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/soumith/convnet-benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sovit-123/Deep-Learning-Image-Captioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/soyers/ddff-pytorch
https://github.com/spacewalk01/depth-anything-tensorrt
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
https://github.com/sparse-depth-sensing/sparse-depth-sensing
https://github.com/sppsps/VGG-16-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/srilakshmiv14/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ssundaram21/dreamsim
When Does Perceptual Alignment Benefit Vision Representations?
https://github.com/stefkim/stylegan-batik
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/strivebo/image_segmentation_dl
Hard Pixel Mining for Depth Privileged Semantic Segmentation
https://github.com/stunback/DenseDepth-paddle
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunjungAn/Tensorflow2.x-CV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnysoni97/single_image_fl
https://github.com/susomena/DeepSlowMotion
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/svdesai/heading-date-estimation
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.