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/Nikolai10/FSQ
https://github.com/Noopuragr/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/OML-Team/open-metric-learning
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/OniroAI/MonoDepth-PyTorch
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/OpenSpaceAI/EC-Depth
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaddlePaddle/PASSL
https://github.com/PaddlePaddle/Paddle3D
Categorical Depth Distribution Network for Monocular 3D Object Detection
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PeterTor/sparse_convolution
https://github.com/PhilippSchmaelzle/mono_depth
https://github.com/Podidiving/lgsc-for-fas-pytorch
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Pomu0708/PyDnet
Towards real-time unsupervised monocular depth estimation on CPU
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
https://github.com/RicardooYoung/LatentDistributionAdjusting
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/Robotmurlock/Motrack
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
https://github.com/Rohed/ml-1
https://github.com/Ruby1302/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Ruby1302/DenseDepth_1
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RuijieZhu94/mmdepth
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ruturaj123/VGG16-Keras
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.