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/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mingxingtan/efficientnet
https://github.com/mjhucla/TF-mRNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjhucla/mRNN-CR
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjoannou/audiovisual-moments-in-time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection
https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection
https://github.com/mnguyen0226/image-augmentation-dnn-performance
https://github.com/mnikitin/Learn-Convolutional-Neural-Network-for-Face-Anti-Spoofing
https://github.com/mobilesec/arcface-tensorflowlite
https://github.com/moddy2024/resnet-9
https://github.com/modelhub-ai/vgg-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/modestyachts/ImageNetV2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohammedsb/dinov2formedical
https://github.com/mohsaad/Deeper-Depth-Prediction
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/mortezagolzan/Face-Anti-Spoofing
https://github.com/mr-bulb/DL_basic_github
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mrpintime/Face-Liveness-TensorFlow
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/mrzzy/np-dl-assign-1
https://github.com/mshilpaa/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/mszpc/3d_dense
https://github.com/mszpc/resnet18
https://github.com/musifahamran/FYP
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/muskie82/CNN-DSO
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/nagadomi/kaggle-cifar10-torch7
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/naver/mast3r
https://github.com/ndb796/pytorch-adversarial-training-cifar
https://github.com/neerajvashistha/vgg16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/neurai-lab/mt-sfmlearner
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics
https://github.com/ngonthier/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nianticlabs/simplerecon
https://github.com/nicaogr/multiresolution_texture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nicku-a/Weather_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.