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/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Saiyam26/Deep-Pixel-wise-Binary-Supervision
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/Saiyam26/Face-Anti-Spoofing-using-DeePixBiS
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-KERAS
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sangkwun/Resnet
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/Seojiyoung/Depth-Map-Estimation
https://github.com/SethEBaldwin/Resnet
https://github.com/SforAiDl/vformer
https://github.com/SharhadBashar/Depth-Percetion
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/ShawnNew/shape-alpha-matting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Shiaoming/vid2depth
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
https://github.com/Shivamt99/Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ShreyasArthur/StyleGAN-2-with-Urban-Plans
https://github.com/ShreyasSkandanS/stereo_sparse_depth_fusion
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SiavashCS/sgan_simple
https://github.com/SimonLBSoerensen/Flexible-Camera-Calibration
Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
https://github.com/SoftwareGift/FeatheNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
https://github.com/SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
https://github.com/Spoorthy-gunda/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SupratimH/neural-style-transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SuryaKalia/AaltoVision
DeMoN: Depth and Motion Network for Learning Monocular Stereo
https://github.com/Sushmitha-Katti/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.