https://github.com/hz-ants/betapose
Estimating 6D Pose From Localizing Designated Surface Keypoints
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image: Zeng et al
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Estimating 6D Pose From Localizing Designated Surface Keypoints
CosyPose: Consistent multi-view multi-object 6D pose estimation
Estimating 6D Pose From Localizing Designated Surface Keypoints
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
CosyPose: Consistent multi-view multi-object 6D pose estimation
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
HOnnotate: A method for 3D Annotation of Hand and Object Poses
HOnnotate: A method for 3D Annotation of Hand and Object Poses
Estimating 6D Pose From Localizing Designated Surface Keypoints
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
Estimating 6D Pose From Localizing Designated Surface Keypoints
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.