https://github.com/hmi88/what
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/honeygupta/UW-Net
https://github.com/hongkunsun/paratranscnn
https://github.com/hongyi-zhang/Fixup
https://github.com/horse007666/ResNet
https://github.com/hperrot/splatting
https://github.com/hsd1503/resnet1d
https://github.com/hsinyilin19/resnetvae
https://github.com/huggingface/transformers
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/4DGaussians
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
https://github.com/hustvl/sparseinst
https://github.com/huyng/incertae
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hyu-cvlab/sweepnet
https://github.com/hz2538/iot_JennyGo
https://github.com/iArunava/ResNet
https://github.com/ibaiGorordo/HITNET-Stereo-Depth-estimation
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
https://github.com/ibaiGorordo/ONNX-GLPDepth
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth
https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/ibaiGorordo/ONNX-MobileStereoNet
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
https://github.com/ibaiGorordo/ONNX-SCDepth-Monocular-Depth-Estimation
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes
https://github.com/ibaiGorordo/ONNX-Unidepth-Monocular-Metric-Depth-Estimation
https://github.com/ibaiGorordo/Sapiens-Pytorch-Inference
https://github.com/ibaiGorordo/TFLite-HITNET-Stereo-depth-estimation
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/ibaiGorordo/TFLite-MobileStereoNet
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
https://github.com/ibaiGorordo/TfLite-Ultra-Fast-Lane-Detection-Inference
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
https://github.com/iitmcvg/CNN_SLAM
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
https://github.com/iitmcvg/redtail
https://github.com/imkoushik22/Walmart-Hackathon
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.