mlsanityData to 14 Oct 2026

Face recognition on point cloud with cgan-top for denoising

arXiv 2506.06864 ↗First posted 7 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Face recognition using 3D point clouds is gaining growing interest, while raw point clouds often contain a significant amount of noise due to imperfect sensors. In this paper, an end-to-end 3D face recognition on a noisy point cloud is proposed, which synergistically integrates the denoising and recognition modules. Specifically, a Conditional Generative Adversarial Network on Three Orthogonal Planes (cGAN-TOP) is designed to effectively remove the noise in the point cloud, and recover the underlying features for subsequent recognition. A Linked Dynamic Graph Convolutional Neural Network (LDGCNN) is then adapted to recognize faces from the processed point cloud, which hierarchically links both the local point features and neighboring features of multiple scales. The proposed method is validated on the Bosphorus dataset. It significantly improves the recognition accuracy under all noise settings, with a maximum gain of 14.81%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 7 Jun 2025.

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15 of 30 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  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. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  5. 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
  6. 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
  7. 2021Score-Based Point Cloud Denoisingcited by 83
  8. 2020Differentiable Manifold Reconstruction for Point Cloud Denoisingcited by 52
  9. 2019Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Featurescited by 31
  10. 2019Feature Boosting Network For 3D Pose Estimationcited by 26
  11. 2017Learning from Millions of 3D Scans for Large-scale 3D Face Recognitioncited by 22
  12. 2018VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classificationcited by 12
3 more
  1. 2018SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projectioncited by 8
  2. 2021Attention-based Dual-stream Vision Transformer for Radar Gait Recognitioncited by 6
  3. 2021Spatial-context-aware deep neural network for multi-class image classificationcited by 4

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