SAPCGAN: Self-attention based generative adversarial network for point clouds

Yushi Li, George Baciu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)

Abstract

The direct extension of 2D image learning to three-dimensional space is 3D point cloud learning. Recently, point cloud learning has shown significant results in 3D shape estimation and semantic segmentation. Despite these advancements, fundamental problems in point cloud learning still pose significant challenges. These problems include representation learning, shape generation, shape segmentation, and shape matching. In this paper, we propose a cognitive self-attention based learning approach to aggregate global representation of 3D shapes from point cloud data. We also integrate 3D point data with a binary tree structure to build a point cloud generator. We further design a novel Generative Adversarial Network (GAN) architecture to generate point clouds resembling the ground truth that could be used for unsupervised learning of 3D shapes. Relying on a self-attention mechanism, our framework, called SAPCGAN, aggregates the global graph features to correct the structural information of 3D shapes in the latent space. Finally, we compare the performance of two gradient penalty methods used in stabilizing the training of our GAN system. We show that our framework has high training efficiency and can provide state-of-the-art results in 3D point cloud generation. The performance of our is demonstrated with both quantitative and qualitative experimental evaluations. Furthermore, the generated 3D point clouds can be segmented into their natural semantic parts, such as, for example the four legs of a chair, the wings of an air plane, or the four wheels of a car.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
EditorsYingxu Wang, Ning Ge, Jianhua Lu, Xiaoming Tao, Paolo Soda, Newton Howard, Bernard Widrow, Jerome Feldman
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-59
Number of pages8
ISBN (Electronic)9781728195940
DOIs
Publication statusPublished - 26 Sept 2020
Externally publishedYes
Event19th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020 - Beijing, China
Duration: 26 Sept 202028 Sept 2020

Publication series

NameProceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020

Conference

Conference19th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
Country/TerritoryChina
CityBeijing
Period26/09/2028/09/20

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