# ArrowPose: Segmentation, Detection, and 5 DoF Pose Estimation Network for Colorless Point Clouds

arXiv: https://arxiv.org/abs/2506.08699 · HTML: https://mlsanity.com/paper/2506.08699/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd](https://mlsanity.com/paper/1512.07506/) (2015)
- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [Photorealistic Image Synthesis for Object Instance Detection](https://mlsanity.com/paper/1902.03334/) (2019)
- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data](https://mlsanity.com/paper/2208.08795/) (2022)
- [A Hybrid Approach for 6DoF Pose Estimation](https://mlsanity.com/paper/2011.05669/) (2020)
- [Deep learning classification of large-scale point clouds: A case study on cuneiform tablets](https://mlsanity.com/paper/2202.10851/) (2022)
- [Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition](https://mlsanity.com/paper/1709.02142/) (2017)
- [Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization](https://mlsanity.com/paper/2011.08517/) (2020)

## Built on by (most cited first, 2 of 2)

- [Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds](https://mlsanity.com/paper/2512.07211/) (2025)
- [A novel network for classification of cuneiform tablet metadata](https://mlsanity.com/paper/2603.03892/) (2026)

## Abstract (arXiv)

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