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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 221–230 of 233 papers

TitleStatusHype
Going Further with Point Pair Features—0
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered ScenesCode1
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search—0
6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality—0
Recovering 6D Object Pose: A Review and Multi-modal Analysis—0
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using DepthCode0
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less ObjectsCode1
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning—0
Global Hypothesis Generation for 6D Object Pose Estimation—0
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation—0
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