# Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor

arXiv: https://arxiv.org/abs/1804.02077 · HTML: https://mlsanity.com/paper/1804.02077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme](https://mlsanity.com/paper/2103.06134/) (2021)

## Abstract (arXiv)

Object retrieval and classification in point cloud data is challenged by noise, irregular sampling density and occlusion. To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy. We further show how the proposed descriptor can be used in a 4D convolutional neural network for the task of object classification. We propose a novel 4D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on 3 benchmark datasets, which confirms the superiority of the proposed approach.