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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 61–70 of 233 papers

TitleStatusHype
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose EstimationCode1
Revisiting Fully Convolutional Geometric Features for Object 6D Pose EstimationCode1
Challenges for Monocular 6D Object Pose Estimation in Robotics—0
YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation—0
TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinement—0
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose EstimationCode1
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects—0
Shape-Constraint Recurrent Flow for 6D Object Pose EstimationCode1
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)Code1
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature MatchingCode0
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