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/ucla-vision/xivo
https://github.com/uclaml/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/udion/DeepUncer
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/ultralytics/yolov5
https://github.com/usnistgov/image-classification-resnet50
https://github.com/usnistgov/image-regression-resnet50
https://github.com/utsawk/CarND-Traffic-Sign-Classifier-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/uw-hai/limeade
https://github.com/varennes/deep-autoencoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/varshaneya/Res-SE-Net
https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/vayuvegula/kerasModels
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vectorinstitute/mmlearn
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://github.com/vendyv/CodeSpeedy_Internship
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vgangal101/VGG_models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vgangal101/resnet_models
https://github.com/victespinoza/NeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vietanhdev/open-adas
https://github.com/vietdhoang/resnet-18
https://github.com/vinay-EIP/EIP-session-5-assignment
https://github.com/vinceecws/Monodepth
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/vinceecws/SegNet_PyTorch
https://github.com/vineeths96/Gradient-Compression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vineeths96/heterogeneous-systems
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vinod377/STN-OCR
https://github.com/vinod377/STN-OCR-Tensorflow
https://github.com/vishal-kataria/MiDaS-master
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/vita-epfl/monstereo
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
https://github.com/vita-epfl/rock-pytorch
https://github.com/vlievin/Unet
https://github.com/voqtuyen/CDCN-Face-Anti-Spoofing.pytorch
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/vpariza/open-hummingbird-eval
https://github.com/vwegn/dm
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wangyuan249/Mymmt767
https://github.com/wasilone11/ICPR-RIP-2024
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.