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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 161–170 of 233 papers

TitleStatusHype
ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation—0
6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning—0
Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation—0
StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation—0
KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation—0
Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks—0
6D Object Pose Estimation using Keypoints and Part Affinity Fields—0
Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation—0
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
MBAPose: Mask and Bounding-Box Aware Pose Estimation of Surgical Instruments with Photorealistic Domain Randomization—0
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