# Visualizing Point Cloud Classifiers by Curvature Smoothing

arXiv: https://arxiv.org/abs/1911.10415 · HTML: https://mlsanity.com/paper/1911.10415/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

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

- [From Heatmaps to Structural Explanations of Image Classifiers](https://mlsanity.com/paper/2109.06365/) (2021)
- [Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations](https://mlsanity.com/paper/2207.12984/) (2022)
- [Representation Learning of Point Cloud Upsampling in Global and Local Inputs](https://mlsanity.com/paper/2501.07076/) (2025)

## Abstract (arXiv)

Recently, several networks that operate directly on point clouds have been proposed. There is significant utility in understanding their mechanisms to classify point clouds, which can potentially help diagnosing these networks and designing better architectures. In this paper, we propose a novel approach to visualize features important to the point cloud classifiers. Our approach is based on smoothing curved areas on a point cloud. After prominent features were smoothed, the resulting point cloud can be evaluated on the network to assess whether the feature is important to the classifier. A technical contribution of the paper is an approximated curvature smoothing algorithm, which can smoothly transition from the original point cloud to one of constant curvature, such as a uniform sphere. Based on the smoothing algorithm, we propose PCI-GOS (Point Cloud Integrated-Gradients Optimized Saliency), a visualization technique that can automatically find the minimal saliency map that covers the most important features on a shape. Experiment results revealed insights into different point cloud classifiers.