# Conditional Invertible Flow for Point Cloud Generation

arXiv: https://arxiv.org/abs/1910.07344 · HTML: https://mlsanity.com/paper/1910.07344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction](https://mlsanity.com/paper/2106.03135/) (2021)
- [FAKEPCD: Fake Point Cloud Detection via Source Attribution](https://mlsanity.com/paper/2312.11213/) (2023)
- [RENs: Relevance Encoding Networks](https://mlsanity.com/paper/2205.13061/) (2022)

## Abstract (arXiv)

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely on parameter sharing among networks, with each cloud having only a small embedding vector that defines it. We use invertible flows networks to generate the individual point clouds, and to regularize the embedding vectors. We evaluate the generative capabilities of the model both in qualitative and quantitative manner.