Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices
Xingjian Yang, Zhitao Yu, Ashis Gopal Banerjee · 2024
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 ColorCode Net (SCCN) embodies a clear and concise pipeline design to effectively address this requirement. SCCN performs pixellevel 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 and LINEMOD Occlusion dataset, respectively, for an NVIDIA Jetson AGX Xavier, while consistently maintaining high estimation accuracy at these rates. The code is available at: https://github.com/smartslab/Sparse-Color-Code-Net.