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/zswang666/PnP-Depth
Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation
https://github.com/zswang666/Stereo-LiDAR-CCVNorm
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
https://github.com/zyf12389/GC-Net
End-to-End Learning of Geometry and Context for Deep Stereo Regression
https://github.com/zzq96/semseggap
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://gitlab.com/birder/birder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://gitlab.com/riccardo2468/srdinet
https://gitlab.com/williamsdaniel888/valma
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.