mlsanityData to 14 Oct 2026

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

arXiv 2506.08699 ↗First posted 10 Jun 2025cs.CVcited by 2

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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

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

17 of 30 references.

References · 17

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  5. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  6. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 222
  7. 2020BOP Challenge 2020 on 6D Object Localizationcited by 193
  8. 2015Recovering 6D Object Pose and Predicting Next-Best-View in the Crowdcited by 79
  9. 2023BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objectscited by 70
  10. 2019Photorealistic Image Synthesis for Object Instance Detectioncited by 66
  11. 2021Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifoldcited by 65
  12. 2024BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objectscited by 60
5 more
  1. 2022An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Datacited by 14
  2. 2020A Hybrid Approach for 6DoF Pose Estimationcited by 13
  3. 2022Deep learning classification of large-scale point clouds: A case study on cuneiform tabletscited by 7
  4. 2017Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognitioncited by 6
  5. 2020Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomizationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Cloudscited by 1
  2. 2026A novel network for classification of cuneiform tablet metadatacited by 0