Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices
<|reference_start|>Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices: As robotics and augmented reality applications increasingly rely on precise and efficient 6D object pose estimation, real-time performance on edge devices is required for more interactive and responsive systems. Our proposed Sparse Color-Code Net (SCCN) embodies a clear and concise pipeline design to effectively address this requirement. SCCN performs pixel-level predictions on the target object in the RGB image, utilizing the sparsity of essential object geometry features to speed up the Perspective-n-Point (PnP) computation process. Additionally, it introduces a novel pixel-level geometry-based object symmetry representation that seamlessly integrates with the initial pose predictions, effectively addressing symmetric object ambiguities. SCCN notably achieves an estimation rate of 19 frames per second (FPS) and 6 FPS on the benchmark LINEMOD dataset and the Occlusion LINEMOD dataset, respectively, for an NVIDIA Jetson AGX Xavier, while consistently maintaining high estimation accuracy at these rates.<|reference_end|>
@article{yang2024sparse,
    title={Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on
  Edge Devices},
    author={Xingjian Yang, Zhitao Yu, and Ashis G. Banerjee},
    journal={arXiv preprint arXiv:2406.02977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02977},
    primaryClass={cs.CV cs.RO}
}