YOLO改进版实现单目6D姿态实时高精度估计
Yolo-Key-6D: Single Stage Monocular 6D Pose Estimation with Keypoint Enhancements
- 用关键点辅助回归提升3D几何理解能力
- 在LINEMOD和LINEMOD-Occluded上分别达96.24%和69.41%准确率
- 端到端设计支持实时推理，适合机器人与XR应用
从单张RGB图像中估计物体的6D姿态是机器人和扩展现实应用中的关键任务。然而，现有主流多阶段方法常因延迟过高而不适用于实时场景。本文提出Yolo-Key-6D，一种基于YOLO架构的单阶段、端到端6D姿态估计框架，兼顾速度与精度。通过引入辅助头部直接回归物体3D边界框角点在图像中的2D投影，显著增强网络对3D几何的理解。为保证稳定训练，采用连续9维表示并经奇异值分解投影至SO(3)空间以回归旋转。在LINEMOD和LINEMOD-Occluded基准上，分别取得96.24%和69.41%的准确率（ADD(-S) 0.1d度量），同时实现实时运行。结果表明，精心设计的单阶段方法可在实际部署中有效平衡性能与效率。
原文摘要 · Abstract (English)
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.
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