Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 31
- 보존된 평가 기록
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Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hiyaroy12/multitask_learning
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/hkpolyu-uav/flvis
Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops
https://github.com/hkust-aerial-robotics/g3reg
Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
https://github.com/hkust-aerial-robotics/vins-fisheye
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
https://github.com/hlzz/DeepMatchVO
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
https://github.com/hnmspirit/malaria_centernet
https://github.com/hoanguyen81190/Synopticon-CenterNet
https://github.com/houssemjebari/Fruit-Detection
https://github.com/hrhodin/NeuralSceneDecomposition
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
https://github.com/hsk9767/open_pose
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
https://github.com/hsp-iit/byogg
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
https://github.com/huggingface/transformers
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/hugozanini/realtime-semantic-segmentation
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/hulianyuyy/adabrowse
Self-Emphasizing Network for Continuous Sign Language Recognition
https://github.com/huochf/StackFLOW
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/hxwork/H2ONet_Pytorch
https://github.com/hyperionfalling/lightpose
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/hyperplane-lab/Generative-3D-Part-Assembly
https://github.com/hz-ants/DenseFusion
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/hz-ants/DensePose
https://github.com/hz-ants/FFB6D
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
https://github.com/hz-ants/HybridPose
HybridPose: 6D Object Pose Estimation under Hybrid Representations
https://github.com/hz-ants/Pix2Pose
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
https://github.com/hz-ants/Pose2Seg
https://github.com/hz-ants/Posecnn
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/hz-ants/SilhoNet
https://github.com/hz-ants/apc-vision-toolbox
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
https://github.com/hz-ants/betapose
Estimating 6D Pose From Localizing Designated Surface Keypoints
https://github.com/hz-ants/cosypose-Base-on-multi-view
CosyPose: Consistent multi-view multi-object 6D pose estimation
https://github.com/hz-ants/obtain-an-object-mesh-and-create-labels
https://github.com/hz-ants/segmentation-driven-pose-train-
Estimating 6D Pose From Localizing Designated Surface Keypoints
https://github.com/hz-ants/yolo-6d
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.