TOWARD UNSUPERVISED 3D POINT CLOUD ANOMALY DETECTION USING VARIATIONAL AUTOENCODER

Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito, Yusuke Sekikawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
出版社IEEE Computer Society
ページ3118-3122
ページ数5
ISBN(電子版)9781665441155
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
継続期間: 2021 9月 192021 9月 22

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2021-September
ISSN(印刷版)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
国/地域United States
CityAnchorage
Period21/9/1921/9/22

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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引用スタイル