# Face recognition on point cloud with cgan-top for denoising

arXiv: https://arxiv.org/abs/2506.06864 · HTML: https://mlsanity.com/paper/2506.06864/

## 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.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Patch-based Progressive 3D Point Set Upsampling](https://mlsanity.com/paper/1811.11286/) (2018)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [Score-Based Point Cloud Denoising](https://mlsanity.com/paper/2107.10981/) (2021)
- [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://mlsanity.com/paper/2007.13551/) (2020)
- [Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features](https://mlsanity.com/paper/1904.10014/) (2019)
- [Feature Boosting Network For 3D Pose Estimation](https://mlsanity.com/paper/1901.04877/) (2019)
- [Learning from Millions of 3D Scans for Large-scale 3D Face Recognition](https://mlsanity.com/paper/1711.05942/) (2017)
- [VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification](https://mlsanity.com/paper/1808.06698/) (2018)
- [SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection](https://mlsanity.com/paper/1811.01571/) (2018)
- [Attention-based Dual-stream Vision Transformer for Radar Gait Recognition](https://mlsanity.com/paper/2111.12290/) (2021)
- [Spatial-context-aware deep neural network for multi-class image classification](https://mlsanity.com/paper/2111.12296/) (2021)

## 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%.