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/liviniuk/DORN_depth_estimation_Pytorch
Deep Ordinal Regression Network for Monocular Depth Estimation
https://github.com/lixihan/Resnet
https://github.com/lixixibj/forecasting-with-time-series-imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lixixibj/tsImage-forecasting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ljy-hy/mentormix_pytorch
https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI
https://github.com/lmb-freiburg/deeptam
https://github.com/lmb-freiburg/demon
DeMoN: Depth and Motion Network for Learning Monocular Stereo
https://github.com/loevlie/DL_Project_PSMNet
https://github.com/loping151/forplane
https://github.com/loping151/lerplane
https://github.com/lornatang/googlenet-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ls-da3m0ns/Short-ResNet
https://github.com/lucidrains/DALLE2-pytorch
https://github.com/lucidrains/En-transformer
https://github.com/lucidrains/linear-attention-transformer
https://github.com/lucidrains/memory-transformer-xl
https://github.com/lucidrains/stylegan2-pytorch
https://github.com/lucidrains/vector-quantize-pytorch
https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lukaszhalbiniak95/Projekt_RA
https://github.com/lxy000719/ResNet-50-pneumonia-classification
https://github.com/ly-zhu/ly-zhu.github.io
Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/macandro96/pytorch-depth-motion
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/magicleap/Atlas
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahmoodlab/hest
https://github.com/makatx/YOLO_ResNet
https://github.com/malin9402/2020-0221
https://github.com/mamoanwar97/Anynet_modified
https://github.com/manankshastri/ART
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.