Secrets of Event-Based Optical Flow

Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego

研究成果: Conference contribution

22 被引用数 (Scopus)

抄録

Event cameras respond to scene dynamics and offer advantages to estimate motion. Following recent image-based deep-learning achievements, optical flow estimation methods for event cameras have rushed to combine those image-based methods with event data. However, it requires several adaptations (data conversion, loss function, etc.) as they have very different properties. We develop a principled method to extend the Contrast Maximization framework to estimate optical flow from events alone. We investigate key elements: how to design the objective function to prevent overfitting, how to warp events to deal better with occlusions, and how to improve convergence with multi-scale raw events. With these key elements, our method ranks first among unsupervised methods on the MVSEC benchmark, and is competitive on the DSEC benchmark. Moreover, our method allows us to expose the issues of the ground truth flow in those benchmarks, and produces remarkable results when it is transferred to unsupervised learning settings. Our code is available at https://github.com/tub-rip/event_based_optical_flow.

本文言語English
ホスト出版物のタイトルComputer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
編集者Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
出版社Springer Science and Business Media Deutschland GmbH
ページ628-645
ページ数18
ISBN(印刷版)9783031197963
DOI
出版ステータスPublished - 2022
イベント17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
継続期間: 2022 10月 232022 10月 27

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13678 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference17th European Conference on Computer Vision, ECCV 2022
国/地域Israel
CityTel Aviv
Period22/10/2322/10/27

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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