# IterativePFN: True Iterative Point Cloud Filtering

arXiv: https://arxiv.org/abs/2304.01529 · HTML: https://mlsanity.com/paper/2304.01529/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [Learning Gradient Fields for Shape Generation](https://mlsanity.com/paper/2008.06520/) (2020)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths](https://mlsanity.com/paper/2012.03408/) (2020)
- [EC-Net: an Edge-aware Point set Consolidation Network](https://mlsanity.com/paper/1807.06010/) (2018)
- [Score-Based Point Cloud Denoising](https://mlsanity.com/paper/2107.10981/) (2021)
- [Point Cloud Upsampling via Disentangled Refinement](https://mlsanity.com/paper/2106.04779/) (2021)
- [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://mlsanity.com/paper/2007.13551/) (2020)
- [Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling](https://mlsanity.com/paper/2002.05968/) (2020)
- [Self-Supervised Pillar Motion Learning for Autonomous Driving](https://mlsanity.com/paper/2104.08683/) (2021)
- [DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares](https://mlsanity.com/paper/2003.10826/) (2020)
- [Learning Graph-Convolutional Representations for Point Cloud Denoising](https://mlsanity.com/paper/2007.02578/) (2020)
- [AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds](https://mlsanity.com/paper/2108.05836/) (2021)
- [PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows](https://mlsanity.com/paper/2203.05940/) (2022)
- [Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching](https://mlsanity.com/paper/2103.16066/) (2021)

## Built on by (most cited first, 28 of 28)

- [P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising](https://mlsanity.com/paper/2408.16325/) (2024)
- [PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding](https://mlsanity.com/paper/2411.00632/) (2024)
- [StraightPCF: Straight Point Cloud Filtering](https://mlsanity.com/paper/2405.08322/) (2024)
- [DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning](https://mlsanity.com/paper/2401.02610/) (2024)
- [ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding](https://mlsanity.com/paper/2502.19247/) (2025)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising](https://mlsanity.com/paper/2503.09283/) (2025)
- [CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving](https://mlsanity.com/paper/2508.13485/) (2025)
- [Joint Point Cloud Upsampling and Cleaning with Octree-based CNNs](https://mlsanity.com/paper/2410.17001/) (2024)
- [U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching](https://mlsanity.com/paper/2510.25210/) (2025)
- [GGPT: Geometry Grounded Point Transformer](https://mlsanity.com/paper/2603.11174/) (2026)
- [Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising](https://mlsanity.com/paper/2502.19660/) (2025)
- [Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering](https://mlsanity.com/paper/2507.03394/) (2025)
- [Hybrid Long and Short Range Flows for Point Cloud Filtering](https://mlsanity.com/paper/2508.08542/) (2025)
- [Deep Learning For Point Cloud Denoising: A Survey](https://mlsanity.com/paper/2508.11932/) (2025)
- [Adaptive and Iterative Point Cloud Denoising with Score-Based Diffusion Model](https://mlsanity.com/paper/2509.14560/) (2025)
- [E3-Net: Efficient E(3)-Equivariant Normal Estimation Network](https://mlsanity.com/paper/2406.00347/) (2024)
- [Neural Octahedral Field: Octahedral prior for simultaneous smoothing and sharp edge regularization](https://mlsanity.com/paper/2408.00303/) (2024)
- [Noise2Score3D:Unsupervised Tweedie's Approach for Point Cloud Denoising](https://mlsanity.com/paper/2502.16826/) (2025)
- [NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision](https://mlsanity.com/paper/2507.13595/) (2025)
- [A Survey of Deep Learning-based Point Cloud Denoising](https://mlsanity.com/paper/2508.17011/) (2025)
- [Class-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation](https://mlsanity.com/paper/2601.12391/) (2026)
- [APC: Transferable and Efficient Adversarial Point Counterattack for Robust 3D Point Cloud Recognition](https://mlsanity.com/paper/2604.15708/) (2026)
- [UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising](https://mlsanity.com/paper/2604.16976/) (2026)
- [Denoising-GS: Gaussian Splatting with Spatial-aware Denoising](https://mlsanity.com/paper/2605.14880/) (2026)
- [PQDT: Pseudo-Query Dual Transformer for Robust Point Cloud Restoration](https://mlsanity.com/paper/2605.25127/) (2026)
- [SIMPC: Learning Self-Induced Mirror-Point Consistency for Unsupervised Point Cloud Denoising](https://mlsanity.com/paper/2605.26894/) (2026)
- [RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation](https://mlsanity.com/paper/2607.23958/) (2026)

## Abstract (arXiv)

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFN network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://github.com/ddsediri/IterativePFN.