# Addressing Overfitting on Pointcloud Classification using Atrous XCRF

## TL;DR

This paper introduces Atrous XCRF, a novel method that reduces overfitting in pointcloud classification by leveraging similarity penalties from unlabeled data, leading to improved generalization.

## Contribution

The paper proposes Atrous XCRF, a new approach that incorporates conditional random field penalties to enhance pointcloud classification accuracy and robustness.

## Key findings

- Achieves 84.97% overall accuracy on ISPRS dataset
- Attains 71.05% F1 score, highest among compared methods
- Performs on par with the best existing models

## Abstract

Advances in techniques for automated classification of pointcloud data introduce great opportunities for many new and existing applications. However, with a limited number of labeled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 84.97% in term of overall accuracy, and 71.05% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score.

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Source: https://tomesphere.com/paper/1902.03088