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Structure guided fusion for depth map inpainting

机译:用于深度图修复的结构导向融合

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摘要

Depth acquisition becomes inexpensive after the revolutionary invention of Kinect. For computer vision applications, depth maps captured by Kinect require additional processing to fill up missing parts. However, conventional inpainting methods for color images cannot be applied directly to depth maps as there are not enough cues to make accurate inference about scene structures. In this paper, we propose a novel fusion based inpainting method to improve depth maps. The proposed fusion strategy integrates conventional inpainting with the recently developed non-local filtering scheme. The good balance between depth and color information guarantees an accurate inpainting result. Experimental results show the mean absolute error of the proposed method is about 20 mm, which is comparable to the precision of the Kinect sensor.
机译:在Kinect的革命性发明之后,深度获取变得廉价。对于计算机视觉应用,由Kinect捕获的深度图需要进行额外的处理以填充丢失的零件。但是,由于没有足够的线索来准确推断场景结构,因此无法将常规的彩色图像修复方法直接应用于深度图。在本文中,我们提出了一种新颖的基于融合的修复方法来改善深度图。提出的融合策略将常规修复与最近开发的非局部过滤方案集成在一起。深度和颜色信息之间的良好平衡可确保准确的修复效果。实验结果表明,该方法的平均绝对误差约为20 mm,与Kinect传感器的精度相当。

著录项

  • 来源
    《Pattern recognition letters》 |2013年第1期|70-76|共7页
  • 作者单位

    School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;

    School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;

    School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;

    School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;

    School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    depth map; inpainting; information fusion; non-local means; kinect;

    机译:深度图修补信息融合;非本地手段;Kinect;

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