Event Collapse in Contrast Maximization Frameworks

Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space–time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models.

Original languageEnglish
Article number5190
JournalSensors
Volume22
Issue number14
DOIs
Publication statusPublished - 2022 Jul

Keywords

  • computer vision
  • contrast maximization
  • event-based camera
  • intelligent sensors
  • motion estimation
  • optical flow
  • robotics

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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