# Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification

arXiv: https://arxiv.org/abs/2010.13114 · HTML: https://mlsanity.com/paper/2010.13114/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds](https://mlsanity.com/paper/2207.11554/) (2022)
- [PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning](https://mlsanity.com/paper/2212.02011/) (2022)
- [OpenPatch: a 3D patchwork for Out-Of-Distribution detection](https://mlsanity.com/paper/2310.03388/) (2023)
- [Feature Adversarial Distillation for Point Cloud Classification](https://mlsanity.com/paper/2306.14221/) (2023)
- [SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds](https://mlsanity.com/paper/2506.13224/) (2025)
- [SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation](https://mlsanity.com/paper/2506.21892/) (2025)

## Abstract (arXiv)

Real-world scenarios pose several challenges to deep learning based computer vision techniques despite their tremendous success in research. Deeper models provide better performance, but are challenging to deploy and knowledge distillation allows us to train smaller models with minimal loss in performance. The model also has to deal with open set samples from classes outside the ones it was trained on and should be able to identify them as unknown samples while classifying the known ones correctly. Finally, most existing image recognition research focuses only on using two-dimensional snapshots of the real world three-dimensional objects. In this work, we aim to bridge these three research fields, which have been developed independently until now, despite being deeply interrelated. We propose a joint Knowledge Distillation and Open Set recognition training methodology for three-dimensional object recognition. We demonstrate the effectiveness of the proposed method via various experiments on how it allows us to obtain a much smaller model, which takes a minimal hit in performance while being capable of open set recognition for 3D point cloud data.