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/JHLee0513/Salt_detection_challenge
https://github.com/JONGSKY/paper
https://github.com/JTRNEO/SynRS3D
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
https://github.com/JahnaviRamagiri/DenseDepth_1
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/JaidedAI/EasyOCR
https://github.com/James-Gilbert-/medical-deep-learning
https://github.com/James-Gilbert-/pneumonia-detection
https://github.com/JanMarcelKezmann/Residual-Network-Architectures
https://github.com/JayPatwardhan/ResNet-PyTorch
https://github.com/JeansonYang/VGG16-MindSpore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JeffreyMagee/Distance-measure-based-on-depth-estimation
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/Jeongyun-Lee-0423/Classification
https://github.com/JesonZhang822/Dogs-VS-Cats-Project
https://github.com/JeyesHan/P2LR
https://github.com/JiangWenPL/multiperson
Coherent Reconstruction of Multiple Humans from a Single Image
https://github.com/JiawangBian/SC-SfMLearner-Release
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
https://github.com/JiaxiongQ/DeepLiDAR
https://github.com/JoeTracey/Portfolio
https://github.com/Jopapy19/CNN-App
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Joshuat38/D-Net
https://github.com/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JuanLuisGonzalez/FAL_net
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
https://github.com/JunjH/Revisiting_Single_Depth_Estimation
https://github.com/JunjH/Visualizing-CNNs-for-monocular-depth-estimation
Visualization of Convolutional Neural Networks for Monocular Depth Estimation
https://github.com/KaimingHe/deep-residual-networks
https://github.com/Kalyan0821/NST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kalyan0821/myelinfoundry-challenge
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarT1994/EmnistRecognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarimaCha/CNN_Trust_Prediction
https://github.com/KarthikGangadhar/depth-estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs
https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10
https://github.com/KellyHwong/rethinking_generalization
https://github.com/King-Otaku/Emotion_Recognition_DNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LIAGM/LFattNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.