ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Clouds
This addresses the problem of efficient object pose estimation in robotics or AR/VR applications, though it appears incremental as it builds on existing methods for colorless point clouds.
The paper tackles fast detection and 5 DoF pose estimation from colorless point clouds by predicting center and top points, achieving state-of-the-art performance on a benchmark dataset and running inference in 250 milliseconds.
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