@inproceedings{e17bb53774404513ad6dd51c93be98b5,
title = "ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Clouds",
abstract = "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",
keywords = "deep learning, point cloud, pose estimation",
author = "Frederik Hagelskjaer",
year = "2025",
month = jun,
doi = "10.1109/ISIE62713.2025.11124654",
language = "English",
series = "Proceedings - IEEE International Symposium on Industrial Electronics (ISIE)",
publisher = "IEEE",
booktitle = "2025 IEEE 34th International Symposium on Industrial Electronics (ISIE)",
address = "United States",
note = "34th IEEE International Symposium on Industrial Electronics, ISIE 2025 ; Conference date: 20-06-2025 Through 23-06-2025",
}