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Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations

arXiv 2409.06267 ↗First posted 10 Sep 2024cs.CVcited by 0

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Abstract (arXiv)

In this paper, we discuss Mahalanobis k-NN: A Statistical Lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted with an arbitrary density of point clouds. We tackle this by adopting Mahalanobis k-NN's inherent property to capture the distribution of the local neighborhood and surficial geometry. Our method can be seamlessly integrated into any local-graph-based point cloud analysis method. In this paper, we focus on two distinct methodologies: Deep Closest Point (DCP) and Deep Universal Manifold Embedding (DeepUME). Our extensive benchmarking on the ModelNet40 and FAUST datasets highlights the efficacy of the proposed method in point cloud registration tasks. Moreover, we establish for the first time that the features acquired through point cloud registration inherently can possess discriminative capabilities. This is evident by a substantial improvement of about 20% in the average accuracy observed in the point cloud few-shot classification task, benchmarked on ModelNet40 and ScanObjectNN.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 54 references that are in the map. First posted on arXiv 10 Sep 2024.

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References · 17

  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. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  5. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  6. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  7. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 231
  8. 2020Deep Global Registrationcited by 228
  9. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  10. 2016Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registrationcited by 159
  11. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
  12. 2020Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Reviewcited by 107
5 more
  1. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  2. 2020DeepGMR: Learning Latent Gaussian Mixture Models for Registrationcited by 91
  3. 2021A comprehensive survey on point cloud registrationcited by 84
  4. 20143D ShapeNets: A Deep Representation for Volumetric Shapescited by 27
  5. 2021DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registrationcited by 1

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