# PC2-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

arXiv: https://arxiv.org/abs/2109.09337 · HTML: https://mlsanity.com/paper/2109.09337/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 59 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 28 of 28)

- [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)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [PU-GAN: a Point Cloud Upsampling Adversarial Network](https://mlsanity.com/paper/1907.10844/) (2019)
- [GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud](https://mlsanity.com/paper/1812.03320/) (2018)
- [Patch-based Progressive 3D Point Set Upsampling](https://mlsanity.com/paper/1811.11286/) (2018)
- [PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks](https://mlsanity.com/paper/1912.03264/) (2019)
- [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)
- [Detail Preserved Point Cloud Completion via Separated Feature Aggregation](https://mlsanity.com/paper/2007.02374/) (2020)
- [PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling](https://mlsanity.com/paper/2002.10277/) (2020)
- [PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval](https://mlsanity.com/paper/1904.09793/) (2019)
- [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://mlsanity.com/paper/2007.13551/) (2020)
- [3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection](https://mlsanity.com/paper/1708.09839/) (2017)
- [Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud](https://mlsanity.com/paper/2102.04317/) (2021)
- [Deep Magnification-Flexible Upsampling over 3D Point Clouds](https://mlsanity.com/paper/2011.12745/) (2020)
- [SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization](https://mlsanity.com/paper/2012.04439/) (2020)
- [SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering](https://mlsanity.com/paper/2108.00454/) (2021)
- [Point Cloud Super Resolution with Adversarial Residual Graph Networks](https://mlsanity.com/paper/1908.02111/) (2019)
- [Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction](https://mlsanity.com/paper/2102.13391/) (2021)
- [Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity](https://mlsanity.com/paper/1705.00949/) (2017)
- ["Zero-Shot" Point Cloud Upsampling](https://mlsanity.com/paper/2106.13765/) (2021)

## Built on by (most cited first, 11 of 11)

- [Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions](https://mlsanity.com/paper/2304.11846/) (2023)
- [Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling](https://mlsanity.com/paper/2312.15133/) (2023)
- [KTNet: Knowledge Transfer for Unpaired 3D Shape Completion](https://mlsanity.com/paper/2111.11976/) (2021)
- [SparseDC: Depth Completion from sparse and non-uniform inputs](https://mlsanity.com/paper/2312.00097/) (2023)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network](https://mlsanity.com/paper/2403.01663/) (2024)
- [EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera](https://mlsanity.com/paper/2412.11680/) (2024)
- [VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution](https://mlsanity.com/paper/2502.12151/) (2025)
- [PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation](https://mlsanity.com/paper/2509.20207/) (2025)
- [PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models](https://mlsanity.com/paper/2508.17050/) (2025)
- [Aerial-ground LiDAR place recognition with patch-level self-supervised learning and expanded reciprocal re-ranking](https://mlsanity.com/paper/2606.18583/) (2026)

## Abstract (arXiv)

Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface. Existing methods divide the input points into small patches and upsample each patch separately, however, ignoring the global spatial consistency between patches. In this paper, we present a novel method PC2-PU, which explores patch-to-patch and point-to-point correlations for more effective and robust point cloud upsampling. Specifically, our network has two appealing designs: (i) We take adjacent patches as supplementary inputs to compensate the loss structure information within a single patch and introduce a Patch Correlation Module to capture the difference and similarity between patches. (ii) After augmenting each patch's geometry, we further introduce a Point Correlation Module to reveal the relationship of points inside each patch to maintain the local spatial consistency. Extensive experiments on both synthetic and real scanned datasets demonstrate that our method surpasses previous upsampling methods, particularly with the noisy inputs. The code and data are at urlhttps://github.com/chenlongwhu/PC2-PU.git.