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/bethgelab/InDomainGeneralizationBenchmark
https://github.com/bhavyagoyal/spclowlight
https://github.com/bo-zhang-cs/CACNet-Pytorch
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
https://github.com/bolianchen/monodepth2_on_nuscenes_cityscapes
https://github.com/bolianchen/pytorch_depth_from_videos_in_the_wild
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
https://github.com/boris127/vehicle-detection
https://github.com/bpagare6/Struct2Depth-ImageFiles
https://github.com/bpagare6/Struct2Depth-Video
https://github.com/br0202/SL-Decoder
Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency
https://github.com/br0202/sensing_area_detection
Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency
https://github.com/brandon-wu76/monocular-depth-estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/brycexu/MR-Residual-Net
https://github.com/btilmon/saccadeCam
SaccadeCam: Adaptive Visual Attention for Monocular Depth Sensing
https://github.com/buyeah1109/KEN
https://github.com/buyeah1109/finc
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cai-jianfeng/ROMP_mindspore
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/cameronchoi/r3det-copy
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chamorajg/pytorch_depth_and_motion_planning
https://github.com/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/chen742/PiPa
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chldydgh4687/2020-1.VideoCaptioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chosj95/PFPNet.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chrhenning/hypercl
https://github.com/chriswxho/dynamic-inference
https://github.com/chshin10/epinet
https://github.com/cj-mclaughlin/segmentation_research
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.