WarpingGAN : Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
PublisherIEEE
Pages6397-6405
Number of pages9
Publication statusPublished - 23 Jun 2022

Conference

Title2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
LocationHybrid
PlaceUnited States
CityNew Orleans
Period19 - 24 June 2022

Abstract

We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, WarpingGAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also ingeniously utilize the principle of the discriminator and tailor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel generating mechanism, WarpingGAN, a single lightweight network after onetime training, is capable of efficiently generating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods to a large extent in terms of quantitative metrics, visual quality, and efficiency. The source code is publicly available at https://github.com/yztang4/WarpingGAN.git.

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Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

WarpingGAN : Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation. / Tang, Yingzhi; Qian, Yue; Zhang, Qijian; Zeng, Yiming; Hou, Junhui; Zhe, Xuefei.

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). IEEE, 2022. p. 6397-6405.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review