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/LakshmiTeja17/NNFL-Project
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/Lebhoryi/learn_monodepth
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/LeonSun0101/CD-SD
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/Lester1711/DSI15_Capstone
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Little-Frog-233/imagecluster
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/tensorflow-vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LouisFoucard/w-net
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
https://github.com/LuigiRussoDev/Covid19Detection
https://github.com/LuigiRussoDev/ResNets
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-06/tree/main/PoseNet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
https://github.com/MS-Mind/MS-Code-08/tree/main/MGN
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
https://github.com/Magicboomliu/Accelerator-Simple-Template
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/MahyarFardin/Monocular_depth_estimation_via_transfer_learning
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/MaliParag/ScanSSD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MarkHershey/arxiv-dl
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/MasazI/cnn_depth_tensorflow
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/MathiasStensrud/autoBleep
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Maveric4/pr19aaw01
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MaximilianStaab/mDFF
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/Mayurji/Image-Classification-PyTorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
https://github.com/Meghraj-Webllisto/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MiX-S/resnet_cifar10
https://github.com/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/Mikoto10032/AutomaticWeightedLoss
https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_seg
https://github.com/Mind23-2/MindCode-101/tree/main/IndexNet
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-3/tree/main/IndexNet
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.