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/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yanglei18/mix-teaching
Objects are Different: Flexible Monocular 3D Object Detection
https://github.com/yangorwell/NGPlus
https://github.com/yangyucheng000/Paper-3/tree/main/mind-spore_-ged-main
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yashclone999/ResNet_MODEL
https://github.com/ycao5602/SAL
https://github.com/yczhang1017/SSD_resnet_pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yhlleo/DeepSegmentor
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yichen928/freesel
https://github.com/yifjiang/relative-depth-using-pytorch
https://github.com/yindaz/DeepCompletionRelease
https://github.com/yjzhai-cs/vehicle-10
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/yqyao/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yuniw18/Joint_360depth
https://github.com/yuvrajarora/CSCI5922_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yw0nam/DenseNet
https://github.com/yxgeee/MMT
https://github.com/yxuhan/adampi
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images
https://github.com/zhangzheyu13/SingleViewDepth
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
https://github.com/zhaozixiang1228/mmif-cddfuse
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
https://github.com/zheng-ningxin/Pruning-from-scratch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/zhengqili/MegaDepth
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
https://github.com/zhengtr/CS231N-Final-Project
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/zhu-xlab/softcon
https://github.com/zhyever/patchrefiner
https://github.com/zjZSTU/ResNet
https://github.com/zjjMaiMai/TinyHITNet
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/zjsong/sspl
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/zju-vipa/KamalEngine
https://github.com/zju3dv/ENeRF
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.