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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.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

Depth Estimation

코드

https://github.com/2023-MindSpore-1/ms-code-177

https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir

https://github.com/2023-MindSpore-1/ms-code-201

https://github.com/2023-MindSpore-1/ms-code-208

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IndexNet

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PoseNet

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ReIDStrongBaseline

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34

https://github.com/2023-MindSpore-1/ms-code-22

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/AdaBin

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea

https://github.com/2023-MindSpore-1/ms-code-49

https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_seg

https://github.com/2023-MindSpore-4/Code-5/tree/main/IndexNet

https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir

https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet

https://github.com/2023-MindSpore-4/Code10/tree/main/VGG

https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning

https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50

https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34

https://github.com/2023-MindSpore-4/Code2/tree/main/darknet53

https://github.com/2023-MindSpore-4/Code3/tree/main/DepthNet

https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline

https://github.com/2023-MindSpore-4/Code7/tree/main/STPM

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34

https://github.com/2023-MindSpore-4/Code8/tree/main/AdaBin

https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet

https://github.com/2023-MindSpore-4/Code9/tree/main/VGG

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dinov2

https://github.com/2024-MindSpore-1/Code5/tree/main/AdaBin

https://github.com/2024-MindSpore-1/Code6/tree/main/AdaBin

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101