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/kreimanlab/HumanIntentionInferenceZeroShot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kvmanohar22/sparse_depth_sensing
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kynkaat/improved-precision-and-recall-metric
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/l0stpenguin/DenseDepthResNet
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/l1997i/lim3d
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
https://github.com/l1997i/rapid_seg
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lakshjaisinghani/Estimating-Depth-from-RGB-and-Sparse-Sensing
https://github.com/lakshjaisinghani/pseudo-lidar-pipeline
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
https://github.com/lakshjaisinghani/pseudo-lidar-ros-pipeline
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
https://github.com/laoreja/CS231A-project-Stereo-matching
End-to-End Learning of Geometry and Context for Deep Stereo Regression
https://github.com/laoshiwei/face-anti-spoofing
Exploiting temporal and depth information for multi-frame face anti-spoofing
https://github.com/lasinger/3DVideos2Stereo
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/leebebeto/pytorch-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lemon-liley/isabella
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
https://github.com/leongatys/DeepTextures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leongatys/PytorchNeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lhoangan/eden-generation
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes
https://github.com/libreai/neural-painters-x
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lightly-ai/lightly
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://github.com/likesum/prdepth
Deep Ordinal Regression Network for Monocular Depth Estimation
https://github.com/lina-lln/adds-depthnet
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/liuyao12/ConvNets-PDE-perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.