Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor

Dmytro Bobkov; Sili Chen; Ruiqing Jian; Muhammad Zafar Iqbal; Eckehard G. Steinbach

RA-L 201821 citations

Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor

Dmytro Bobkov, Sili Chen, Ruiqing Jian, Muhammad Zafar Iqbal, Eckehard G. Steinbach

Abstract

Object retrieval and classification in point cloud data are 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 four-dimensional (4-D) convolutional neural network for the task of object classification. We propose a novel 4-D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on three benchmark datasets, which confirms the superiority of the proposed approach.

BibTeX

@inproceedings{ral2018_noiseresistantde,
  title = {Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor},
  author = {Dmytro Bobkov and Sili Chen and Ruiqing Jian and Muhammad Zafar Iqbal and Eckehard G. Steinbach},
  booktitle = {RA-L 2018},
  year = {2018}
}