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6D Pose Estimation using RGB

6D Pose Estimation using RGB refers to the task of determining the six degree-of-freedom (6D) pose of an object in 3D space based on RGB images. This involves estimating the position and orientation of an object in a scene, and is a fundamental problem in computer vision and robotics. In this task, the goal is to estimate the 6D pose of an object given an RGB image of the object and the scene, which can be used for tasks such as robotic manipulation, augmented reality, and scene reconstruction.

( Image credit: Segmentation-driven 6D Object Pose Estimation )

Papers

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Showing 126–150 of 233 papers

TitleStatusHype
Object Pose Estimation in Robotics Revisited—0
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects—0
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects—0
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation—0
Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusion—0
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset—0
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data—0
Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks—0
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation—0
Category-level 6D Object Pose Recovery in Depth Images—0
Category-Level and Open-Set Object Pose Estimation for Robotics—0
CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation—0
Center-Based Decoupled Point-cloud Registration for 6D Object Pose Estimation—0
Challenges for Monocular 6D Object Pose Estimation in Robotics—0
Conscious Inference for Object Detection—0
Context-aware 6D Pose Estimation of Known Objects using RGB-D data—0
ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation—0
COPE: End-to-end trainable Constant Runtime Object Pose Estimation—0
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation—0
EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation—0
End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints—0
End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation—0
Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection—0
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction—0
Extending 6D Object Pose Estimators for Stereo Vision—0
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