Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 14
- 보존된 평가 기록
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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/3P2S/arcface
https://github.com/3dperceptionlab/therobotrix
https://github.com/96jonesa/StyleGan2-Colab-Demo
https://github.com/A-Jacobson/Depth_in_The_Wild
https://github.com/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AaltoML/GP-MVS
https://github.com/AbdallahMGomaa/PatchNet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/AbhinavAtrishi/semisupervised-multitask-learning
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/AbirKhan96/Intel-ISL-MiDaS
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/AdamHtooLwin/vigilant
https://github.com/Adelaide-AI-Group/ST-CLSTM
Exploiting temporal consistency for real-time video depth estimation
https://github.com/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Akilax0/Pseudo-RGB-D-for-Self-Improving-Monocular-SLAM-and-Depth-Prediction
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
https://github.com/AleksBanbur/EE8204---Real-Time-Multi-Task-Learning
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
https://github.com/AmeetR/Monocular-Depth-Estimation-DAV
Guiding Monocular Depth Estimation Using Depth-Attention Volume
https://github.com/AmirsSaad/UWPhysDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AndreiMoraru123/Hydra
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Arthur151/CenterHMR
Putting People in their Place: Monocular Regression of 3D People in Depth
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BR-IDL/PaddleViT/tree/main/semantic_segmentation
https://github.com/Baichenjia/Resnet
https://github.com/Bakikii/stylegan2-pytorch23
https://github.com/BalajiAJ/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Bao-Jiarong/ResNet
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/BurguerJohn/global_perceptual_similarity_loss
https://github.com/BurguerJohn/torch-felix
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.