mlsanityData to 7 Oct 2026

PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation

arXiv 2202.07843 ↗First posted 16 Feb 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work. It is assumed that there exists a gallery point cloud set that contains point cloud objects with given pose orientation information. PCRP attempts to register the unknown point cloud object with those in the gallery set so as to achieve content-based object retrieval and pose estimation jointly, where the point cloud registration task is built upon an enhanced version of the unsupervised R-PointHop method. Experiments on the ModelNet40 dataset demonstrate the superior performance of PCRP in comparison with traditional and learning based methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

18 of 28 references.

References · 18

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  5. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  6. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  7. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 231
  8. 2018PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognitioncited by 197
  9. 2018The Perfect Match: 3D Point Cloud Matching with Smoothed Densitiescited by 169
  10. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
  11. 2018PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptorscited by 140
  12. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
6 more
  1. 2019PointHop: An Explainable Machine Learning Method for Point Cloud Classificationcited by 46
  2. 2021Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimationcited by 45
  3. 2021R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Methodcited by 26
  4. 2020PointHop++: A Lightweight Learning Model on Point Sets for 3D Classificationcited by 22
  5. 2020Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)cited by 19
  6. 2021CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registrationcited by 5

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.

  1. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  2. 2023S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classificationcited by 3
  3. 2023PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Cloudscited by 1
  4. 2023A Tiny Machine Learning Model for Point Cloud Object Classificationcited by 0