Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices

• URL: http://arxiv.org/abs/2406.02977v1
• Date: Wed, 5 Jun 2024 06:21:48 GMT
• Title: Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices
• Authors: Xingjian Yang, Zhitao Yu, Ashis G. Banerjee,
• Abstract summary: Our proposed Color-Code Net ( SCCN) embodies a clear and concise pipeline design to 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 process. It notably achieves an estimation rate of 19 frames per second (FPS) and 6 FPS on the benchmark LINEMOD dataset and the OcclusionMOD dataset.
• Score: 2.3281513013731145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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