YOLOPose | Object Pose Estimation

Object Pose Estimation · 2022 · arXiv · CCF Unranked

YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression

Uses a Transformer for joint detection and keypoint regression, aiming to estimate detections and 6D poses for multiple objects end-to-end within a single-stage architecture.

Paper

Research area

Object Pose Estimation

Task level

Instance-level

Object setting

Seen objects

Input modalities

RGB

Requirements

camera intrinsicsCAD model2D keypoints

Full paper metadata Authors, affiliations, research area, datasets, metrics, method tags, and reading status Expand

Authors: Arash Amini, Arul Selvam Periyasamy, Sven Behnke
Affiliations: University of Bonn
Conference / journal: Preprint · arXiv · CCF Unranked
Research area: Object Pose Estimation
Code availability: Unavailable / not yet recorded
Reading status: Overview:Pending Deep Dive:Pending
Task setting: Instance-level · Seen objects
Datasets: LINEMOD Occlusion LINEMOD
Evaluation metrics: ADD ADD-S 2D Projection
Method tags: Keypoints + PnP Synthetic Data & Foundation Models Multi-object Pose Estimation Instance-level Pose Estimation Absolute Pose Estimation
Requirements: camera intrinsicsCAD model2D keypoints

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