スパースカラーコードネット：エッジデバイス上でのリアルタイムRGBベース6D物体姿勢推定
Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices
RGB画像から物体の6D姿勢を推定する際、物体の幾何特徴のスパース性を利用してPnP計算を高速化し、対称物体の曖昧さも解決する手法を提案。エッジデバイス上で高精度かつリアルタイム（LINEMODで19FPS）に動作する。
著者: Xingjian Yang, Zhitao Yu, Ashis G. Banerjee
分類: cs.CV, cs.RO
原文アブストラクト
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.
