# Spectral-GANs for High-Resolution 3D Point-cloud Generation

arXiv: https://arxiv.org/abs/1912.01800 · HTML: https://mlsanity.com/paper/1912.01800/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning](https://mlsanity.com/paper/2410.08114/) (2024)
- [SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation](https://mlsanity.com/paper/2108.04476/) (2021)
- [Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks](https://mlsanity.com/paper/2202.07261/) (2022)
- [CAD: Photorealistic 3D Generation via Adversarial Distillation](https://mlsanity.com/paper/2312.06663/) (2023)
- [Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing](https://mlsanity.com/paper/2207.13326/) (2022)
- [LPF-Defense: 3D Adversarial Defense based on Frequency Analysis](https://mlsanity.com/paper/2202.11287/) (2022)
- [Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection](https://mlsanity.com/paper/2305.01652/) (2023)
- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [Towards Controllable Diffusion Models via Reward-Guided Exploration](https://mlsanity.com/paper/2304.07132/) (2023)
- [PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors](https://mlsanity.com/paper/2211.08702/) (2022)
- [Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model](https://mlsanity.com/paper/2202.06407/) (2022)
- [Deep Generative Models on 3D Representations: A Survey](https://mlsanity.com/paper/2210.15663/) (2022)
- [3D GANs and Latent Space: A comprehensive survey](https://mlsanity.com/paper/2304.03932/) (2023)
- [STS-Mixer: Spatio-Temporal-Spectral Mixer for 4D Point Cloud Video Understanding](https://mlsanity.com/paper/2604.11637/) (2026)
- [Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning](https://mlsanity.com/paper/2305.00510/) (2023)
- [SM3D: Mitigating Spectral Bias and Semantic Dilution in Point Cloud State Space Models](https://mlsanity.com/paper/2505.11099/) (2025)
- [Synthetic-to-Real Translation for Class-Agnostic Motion Prediction](https://mlsanity.com/paper/2607.06319/) (2026)

## Abstract (arXiv)

Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synthesize and reconstruct high-quality point-clouds. Current deep generative models for 3D data generally work on simplified representations (e.g., voxelized objects) and cannot deal with the inherent redundancy and irregularity in point-clouds. A few recent efforts on 3D point-cloud generation offer limited resolution and their complexity grows with the increase in output resolution. In this paper, we develop a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN). Our spectral representation is highly structured and allows us to disentangle various frequency bands such that the learning task is simplified for a GAN model. As compared to spatial-domain generative approaches, our formulation allows us to generate arbitrary number of points high-resolution point-clouds with minimal computational overhead. Furthermore, we propose a fully differentiable block to transform from {the} spectral to the spatial domain and back, thereby allowing us to integrate knowledge from well-established spatial models. We demonstrate that Spectral-GAN performs well for point-cloud generation task. Additionally, it can learn {a} highly discriminative representation in an unsupervised fashion and can be used to accurately reconstruct 3D objects.