# Point Cloud Processing via Recurrent Set Encoding

arXiv: https://arxiv.org/abs/1911.10729 · HTML: https://mlsanity.com/paper/1911.10729/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network](https://mlsanity.com/paper/2104.07861/) (2021)
- [A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation](https://mlsanity.com/paper/2405.11903/) (2024)
- [CloudWalker: Random walks for 3D point cloud shape analysis](https://mlsanity.com/paper/2112.01050/) (2021)
- [TACO-Net: Topological Signatures Triumph in 3D Object Classification](https://mlsanity.com/paper/2509.24802/) (2025)
- [A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2605.17131/) (2026)

## Abstract (arXiv)

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation. Our network is effective at spatial feature learning, and competes favorably with the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is significantly more efficient compared to the SOTAs.