Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices
This addresses the problem of real-time object pose estimation in cluttered environments for robotics and AR/VR applications, representing an incremental improvement through integration of existing components.
The paper tackles robust 6D pose estimation and tracking of novel objects under challenging illumination by developing a unified framework with a shared color-pair feature representation, achieving competitive accuracy and high-fidelity tracking on edge devices.
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.