  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.
