快速精准估计无色点云中物体的5自由度位姿
ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Clouds
- 基于中心与顶部点预测实现5自由度位姿估计
- 在基准数据集上达到当前最优性能，推理仅需250毫秒
- 适用于实时场景，适合工业检测等实际应用
本文提出一种针对无色点云的快速检测与5自由度（DoF）位姿估计网络。该网络通过神经网络预测物体的中心点与顶点，从而计算其位姿。模型在合成数据上训练，并在基准数据集上进行测试，表现出当前最佳性能，显著优于所有无色点云方法。该网络推理时间仅为250毫秒，具备实际部署潜力。项目主页与代码见 arrowpose.github.io。
原文摘要 · Abstract (English)
This paper presents a fast detection and 5 DoF (Degrees of Freedom) pose estimation network for colorless point clouds. The pose estimation is calculated from center and top points of the object, predicted by the neural network. The network is trained on synthetic data, and tested on a benchmark dataset, where it demonstrates state-of-the-art performance and outperforms all colorless methods. The network is able to run inference in only 250 milliseconds making it usable in many scenarios. Project page with code at arrowpose.github.io
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