Papers with Code 아카이브
6D Pose Estimation using RGBD
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
GenPose: Generative Category-level Object Pose Estimation via Diffusion Models
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
Generative Category-Level Shape and Pose Estimation with Semantic Primitives
A Multi-body Tracking Framework -- From Rigid Objects to Kinematic Structures
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency
BOP Challenge 2020 on 6D Object Localization
EPOS: Estimating 6D Pose of Objects with Symmetries
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression
MaskedFusion: Mask-based 6D Object Pose Estimation
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
BOP: Benchmark for 6D Object Pose Estimation
DeepIM: Deep Iterative Matching for 6D Pose Estimation
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.