A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. A novel, computationally efficient regularizer based on geometric principles to mitigate event collapse is proposed. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, this regularizer is the only effective solution for event collapse without trading off the runtime. It is hoped that this work opens the door for future applications that unlocks the advantages of event cameras. Project page: https://github.com/tub-rip/event_collapse.

本文言語English
論文番号2200251
ジャーナルAdvanced Intelligent Systems
5
3
DOI
出版ステータスPublished - 2023 3月

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用
  • 機械工学
  • 制御およびシステム工学
  • 電子工学および電気工学
  • 材料科学(その他)

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