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/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/highrescanopyheight
https://github.com/facebookresearch/perception_models
Perception Encoder: The best visual embeddings are not at the output of the network
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/sapiens
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facundoq/transformational_measures_experiments
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/faheinrich/moon_pose_estimation_setup
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
https://github.com/faheinrich/moon_pose_with_detectron2
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
https://github.com/fangchangma/sparse-to-dense.pytorch
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/fangzong12/FeatherNet
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
https://github.com/fastai/fastai
https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/fengredrum/cnn-xla
https://github.com/ferdyandannes/Train-struct2depth
https://github.com/ferdyandannes/struct2depth_train
https://github.com/ferdyandannes/struct2depthv2
https://github.com/ffletcherr/face-recognition-liveness
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fraunhoferhhi/X-maps
X-maps: Direct Depth Lookup for Event-based Structured Light Systems
https://github.com/freshtan/midas_v2
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/frobelbest/BANet
https://github.com/fuy34/dfv
https://github.com/fzi-forschungszentrum-informatik/mrf
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
https://github.com/gaasher/I-JEPA
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://github.com/gameover27/ddff-pytorch
https://github.com/gatheluck/Stronghold
https://github.com/gcr/torch-residual-networks
https://github.com/gdlg/panoramic-depth-estimation
https://github.com/gentlemanman/fcrn_pytorch
Deeper Depth Prediction with Fully Convolutional Residual Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.