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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 191–200 of 233 papers

TitleStatusHype
GS2Pose: Two-stage 6D Object Pose Estimation Guided by Gaussian Splatting—0
GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting—0
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects—0
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation—0
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data—0
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects—0
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation—0
Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation—0
Improving 6D Object Pose Estimation of metallic Household and Industry Objects—0
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search—0
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