mlsanityData to 7 Oct 2026

An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure

arXiv 2207.05112 ↗First posted 11 Jul 2022cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a new method for determining shared features of and measuring the distance between data sets or point clouds. Our approach uses the joint factorization of two data matrices X1,X2 into non-negative matrices X1 = AS1, X2 = AS2 to derive a similarity measure that determines how well the shared basis A approximates X1, X2. We also propose a point cloud distance measure built upon this method and the learned factorization. Our method reveals structural differences in both image and text data. Potential applications include classification, detecting plagiarism or other manipulation, data denoising, and transfer learning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

9 of 36 references.

References · 9

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  3. 2020Geometric Dataset Distances via Optimal Transportcited by 157
  4. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 111
  5. 2019JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fieldscited by 62
  6. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  7. 2020DPDist : Comparing Point Clouds Using Deep Point Cloud Distancecited by 22
  8. 2020A generalized Hausdorff distance based quality metric for point cloud geometrycited by 15
  9. 2020Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distancecited by 6

Built on by

Not cited yet.