# NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler

arXiv: https://arxiv.org/abs/1901.09394 · HTML: https://mlsanity.com/paper/1901.09394/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2202.13589/) (2022)
- [Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views](https://mlsanity.com/paper/2108.03743/) (2021)
- [Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds](https://mlsanity.com/paper/2102.04136/) (2021)
- [A stepped sampling method for video detection using LSTM](https://mlsanity.com/paper/2107.08471/) (2021)

## Abstract (arXiv)

Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require large networks parameterized by many weights, which makes them hard to train. In this paper, we propose an auto-encoder architecture that can both encode and decode clouds of arbitrary size and demonstrate its effectiveness at upsampling sparse point clouds. Interestingly, we can do so using less than half as many parameters as state-of-the-art architectures while still delivering better performance. We will make our code base fully available.