提出光照不变的配色特征，实现边缘设备上高精度6D位姿估计与跟踪。
Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices
- 用共享的配色特征统一初始估计与运动追踪，减少计算开销。
- 在真实光照变化下仍保持高精度，动态姿态变化中跟踪稳定。
- 适合部署于资源受限的边缘设备，兼顾速度与鲁棒性。
在复杂光照条件下对新物体进行鲁棒的6D位姿估计仍是重大挑战，通常需在初始位姿精度与实时追踪效率间权衡。本文提出一个专为边缘设备设计的统一框架，融合鲁棒的初始估计模块与快速运动追踪器。核心在于一种共享的、光照不变的配色特征表示，为两个阶段提供一致基础。初始估计阶段，该特征实现实时RGB-D图像与物体3D网格间的鲁棒匹配；追踪阶段，同一特征逻辑验证时序对应关系，使轻量模型可可靠回归物体运动。在多个基准数据集上的实验表明，该方法兼具有效性与鲁棒性，在极端光照变化和快速姿态跳跃下仍能保持高保真度跟踪，且定位精度具有竞争力。
原文摘要 · Abstract (English)
Robust 6D pose estimation of novel objects under challenging illumination remains a significant challenge, often requiring a trade-off between accurate initial pose estimation and efficient real-time tracking. We present a unified framework explicitly designed for efficient execution on edge devices, which synergizes a robust initial estimation module with a fast motion-based tracker. The key to our approach is a shared, lighting-invariant color-pair feature representation that forms a consistent foundation for both stages. For initial estimation, this feature facilitates robust registration between the live RGB-D view and the object's 3D mesh. For tracking, the same feature logic validates temporal correspondences, enabling a lightweight model to reliably regress the object's motion. Extensive experiments on benchmark datasets demonstrate that our integrated approach is both effective and robust, providing competitive pose estimation accuracy while maintaining high-fidelity tracking even through abrupt pose changes.
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