Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices
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| Format: | Preprint |
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2024
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| _version_ | 1866929374763155456 |
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| author | Yang, Xingjian Yu, Zhitao Banerjee, Ashis G. |
| author_facet | Yang, Xingjian Yu, Zhitao Banerjee, Ashis G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices Yang, Xingjian Yu, Zhitao Banerjee, Ashis G. Computer Vision and Pattern Recognition Robotics 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. |
| title | Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2406.02977 |