# Noise Injection-based Regularization for Point Cloud Processing

**Authors:** Xiao Zang, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan  
**arXiv:** [2103.15027](https://arxiv.org/abs/2103.15027)  
**Date:** 2021-03-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2103.15027v1/

## Key points

- Noise injection-based regularization techniques like Dropout have been extensively used in deep neural networks (DNNs) in the image domain for performance enhancement.
- Application of efficient regularization methods in the point cloud domain has been relatively underexplored.
- Study by Xiao Zang, Yi Xie, Siyu Liao, Jie Chen, and Bo Yuan focused on noise injection-based regularization tailored for DNNs operating in the point cloud domain.
- Introduced novel techniques named DropFeat, DropPoint, and DropCluster involving noise injection at feature level, point level, and cluster level respectively.
- Empirical analysis on factors like dropping rate, cluster size, and dropping position provided valuable insights and established deployment guidelines across diverse datasets and DNN architectures.
- Experimental results showed significant performance improvements with techniques like DropCluster leading to higher overall accuracy rates for popular backbone DNNs such as PointNet, PointNet++, and DGCNN on datasets like ModelNet40 shape classification dataset and S3DIS semantic segmentation dataset.
- Notably improved overall accuracy rates by 1.5%, 1.3%, and 0.8% for PointNet++, PointNet++, and DGCNN respectively on ModelNet40 dataset; while enhancing mean Intersection-over-union (IoU) values by 3.2%, 2.9%, and 3.7% for PointNet++, PointNet++, and DGCNN respectively on S3DIS semantic segmentation dataset.
- Research highlights the effectiveness of noise injection-based regularization techniques in improving DNN performance in the point cloud domain with insights for future developments in this area.

## Summary

In the field of deep neural networks (DNNs), noise injection-based regularization techniques like Dropout have been extensively utilized in the image domain to enhance performance. However, the application of efficient regularization methods in the point cloud domain has been relatively underexplored. Recently, a study by Xiao Zang, Yi Xie, Siyu Liao, Jie Chen and Bo Yuan delved into this topic and conducted a systematic investigation into noise injection-based regularization specifically tailored for DNNs operating in the point cloud domain. They introduced novel techniques named DropFeat, DropPoint and DropCluster that involve injecting noise at different levels - feature level, point level and cluster level respectively. Through empirical analysis on various factors such as dropping rate, cluster size and dropping position, they gained valuable insights and established general deployment guidelines for their methods across diverse datasets and DNN architectures. The experimental results showed significant performance improvements achieved through their techniques. Notably, DropCluster provided higher overall accuracy rates for popular backbone DNNs like PointNet, PointNet++ and DGCNN on datasets such as ModelNet40 shape classification dataset, ShapeNet part segmentation dataset and S3DIS semantic segmentation dataset. For instance, it led to an increase of 1.5%, 1.3% and 0.8% in overall accuracy for PointNet++, PointNet++ and DGCNN respectively on ModelNet40 dataset; while on S3DIS semantic segmentation dataset it improved mean Intersection-over-union (IoU) values by 3.2%, 2.9% and 3.7% for PointNet++, PointNet++ and DGCNN respectively. This research sheds light on the effectiveness of noise injection-based regularization techniques in enhancing the performance of DNNs operating in the point cloud domain and provides valuable insights for future developments in this area of study.