# Point Cloud Super Resolution with Adversarial Residual Graph Networks

arXiv: https://arxiv.org/abs/1908.02111 · HTML: https://mlsanity.com/paper/1908.02111/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation](https://mlsanity.com/paper/2204.08196/) (2022)
- [Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes](https://mlsanity.com/paper/2312.04043/) (2023)
- [PC2-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling](https://mlsanity.com/paper/2109.09337/) (2021)
- [Super-resolution 3D Human Shape from a Single Low-Resolution Image](https://mlsanity.com/paper/2208.10738/) (2022)
- [HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving](https://mlsanity.com/paper/2202.03901/) (2022)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- ["Zero-Shot" Point Cloud Upsampling](https://mlsanity.com/paper/2106.13765/) (2021)
- [Deep Point Cloud Simplification for High-quality Surface Reconstruction](https://mlsanity.com/paper/2203.09088/) (2022)
- [Parametric Surface Constrained Upsampler Network for Point Cloud](https://mlsanity.com/paper/2303.08240/) (2023)
- [Empir3D : A Framework for Multi-Dimensional Point Cloud Assessment](https://mlsanity.com/paper/2306.03660/) (2023)
- [SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network](https://mlsanity.com/paper/2502.19452/) (2025)
- [2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time](https://mlsanity.com/paper/2511.08224/) (2025)

## Abstract (arXiv)

Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details. In this paper, we present a data-driven method for point cloud super-resolution based on graph networks and adversarial losses. The key idea of the proposed network is to exploit the local similarity of point cloud and the analogy between LR input and HR output. For the former, we design a deep network with graph convolution. For the latter, we propose to add residual connections into graph convolution and introduce a skip connection between input and output. The proposed network is trained with a novel loss function, which combines Chamfer Distance (CD) and graph adversarial loss. Such a loss function captures the characteristics of HR point cloud automatically without manual design. We conduct a series of experiments to evaluate our method and validate the superiority over other methods. Results show that the proposed method achieves the state-of-the-art performance and have a good generalization ability to unseen data.