Yolo-Key-6D: Single Stage Monocular 6D Pose Estimation with Keypoint Enhancements

TL;DR

Yolo-Key-6D is a fast, single-stage monocular 6D pose estimation framework that enhances YOLO with keypoint detection and continuous rotation regression, achieving high accuracy and real-time performance.

Contribution

The paper introduces a novel end-to-end single-stage approach combining keypoint detection and continuous rotation regression for improved 6D pose estimation.

Findings

Achieves 96.24% accuracy on LINEMOD benchmark.

Operates in real time with competitive accuracy.

Effectively balances speed and precision for practical deployment.

Abstract

Estimating the 6D pose of objects from a single RGB image is a critical task for robotics and extended reality applications. However, state-of-the-art multi stage methods often suffer from high latency, making them unsuitable for real time use. In this paper, we present Yolo-Key-6D, a novel single stage, end-to-end framework for monocular 6D pose estimation designed for both speed and accuracy. Our approach enhances a YOLO based architecture by integrating an auxiliary head that regresses the 2D projections of an object's 3D bounding box corners. This keypoint detection task significantly improves the network's understanding of 3D geometry. For stable end-to-end training, we directly regress rotation using a continuous 9D representation projected to SO(3) via singular value decomposition. On the LINEMOD and LINEMOD-Occluded benchmarks, YOLO-Key-6D achieves competitive accuracy scores of 96.24% and 69.41%, respectively, with the ADD(-S) 0.1d metric, while proving itself to operate in real time. Our results demonstrate that a carefully designed single stage method can provide a practical and effective balance of performance and efficiency for real world deployment.