# A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds

arXiv: https://arxiv.org/abs/2010.05391 · HTML: https://mlsanity.com/paper/2010.05391/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation](https://mlsanity.com/paper/2108.04476/) (2021)
- [GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy](https://mlsanity.com/paper/2205.08886/) (2022)
- [PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors](https://mlsanity.com/paper/2211.08702/) (2022)
- [DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation](https://mlsanity.com/paper/2309.14463/) (2023)
- [HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population](https://mlsanity.com/paper/2108.01411/) (2021)
- [Deep Generative Models on 3D Representations: A Survey](https://mlsanity.com/paper/2210.15663/) (2022)
- [3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability](https://mlsanity.com/paper/2307.14051/) (2023)
- [Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning](https://mlsanity.com/paper/2305.00510/) (2023)
- [IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstruction](https://mlsanity.com/paper/2311.02552/) (2023)
- [DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation](https://mlsanity.com/paper/2506.18779/) (2025)

## Abstract (arXiv)

In this paper, we introduce a novel conditional generative adversarial network that creates dense 3D point clouds, with color, for assorted classes of objects in an unsupervised manner. To overcome the difficulty of capturing intricate details at high resolutions, we propose a point transformer that progressively grows the network through the use of graph convolutions. The network is composed of a leaf output layer and an initial set of branches. Every training iteration evolves a point vector into a point cloud of increasing resolution. After a fixed number of iterations, the number of branches is increased by replicating the last branch. Experimental results show that our network is capable of learning and mimicking a 3D data distribution, and produces colored point clouds with fine details at multiple resolutions.