https://github.com/deepjyotisaha85/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
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/deepmind/dm-haiku
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/demul/ResidualNet
https://github.com/denyingmxd/cfpnet
Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular Images
https://github.com/deontaepharr/Residual-Attention-Network
https://github.com/dfan/single-image-surface-normal-estimation
https://github.com/dg-enlens/banet-depth-prediction
Bidirectional Attention Network for Monocular Depth Estimation
https://github.com/dishen12/py03
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/djwhsdj/vw-sdk
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/dmizr/phuber
https://github.com/dnddnjs/pytorch-cifar10
https://github.com/doantienthongbku/Implementation-patchnet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/dontLoveBugs/DORN_pytorch
Deep Ordinal Regression Network for Monocular Depth Estimation
https://github.com/dorsadadjoo/Distance_Estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/dronefreak/dji-tello-collision-avoidance-pydnet
Towards real-time unsupervised monocular depth estimation on CPU
https://github.com/dssbxx/k_labels_resnet50
https://github.com/dsshim0125/dacl
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/duan-song/SATNet
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI
https://github.com/dulucas/simlpe
https://github.com/dwofk/fast-depth
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
https://github.com/ebagdasa/backdoors101
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/endomapper/em_dataset-photometriccalibration
Photometric single-view dense 3D reconstruction in endoscopy
https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch
https://github.com/enzomuschik/distilfnd
https://github.com/eric-erki/IdenProf
https://github.com/ewinata/CIFAR-10
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
https://github.com/fabio-sim/Depth-Anything-ONNX
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.