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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 131–140 of 233 papers

TitleStatusHype
ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation—0
ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity TrackingCode1
Occlusion-Robust Object Pose Estimation with Holistic RepresentationCode1
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
KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation—0
StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation—0
Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks—0
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose EstimationCode1
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D ModelsCode1
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