Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

6 被引用数 (Scopus)

抄録

3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferability is constrained by the training configuration and the training is susceptible to peculiar lossy noises called ray-drop. To address the issue, this paper proposes a generative model of LiDAR range images applicable to the data-level domain transfer. Motivated by the fact that LiDAR measurement is based on point-by-point range imaging, we train an implicit image representation-based generative adversarial networks along with a differentiable ray-drop effect. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models. We also showcase upsampling and restoration applications. Furthermore, we introduce a Sim2Real application for LiDAR semantic segmentation. We demonstrate that our method is effective as a realistic ray-drop simulator and outperforms state-of-the-art methods.

本文言語英語
ホスト出版物のタイトルProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1256-1266
ページ数11
ISBN(電子版)9781665493468
DOI
出版ステータス出版済み - 1月 2023
イベント23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, 米国
継続期間: 1月 3 20231月 7 2023

出版物シリーズ

名前Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

会議

会議23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
国/地域米国
CityWaikoloa
Period1/3/231/7/23

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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