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<p>To create accurate 3D models from video footage, it is essential to use high-quality videos without floating objects that could interfere with the process. In this study, we applied the segment anything model (SAM) and generative image in-painting to enhance the quality of video frames by detecting and removing floating objects on a frame-by-frame basis. The results demonstrated the effectiveness of this approach in detecting and eliminating such objects, contributing to the improvement of video quality.</p>

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Floating object removal in underwater ROV video images using segment anything model and generative image in-painting

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Hiroki Takahashi,
  • Toru Kato,
  • Meguru Yamashita,
  • Akio Doi,
  • Takashi Imabuchi

摘要

To create accurate 3D models from video footage, it is essential to use high-quality videos without floating objects that could interfere with the process. In this study, we applied the segment anything model (SAM) and generative image in-painting to enhance the quality of video frames by detecting and removing floating objects on a frame-by-frame basis. The results demonstrated the effectiveness of this approach in detecting and eliminating such objects, contributing to the improvement of video quality.