<italic style="font-style: italic">K</italic>-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps

Li-wei LIU; Yang LI; Ming ZHANG; Liang-hao WANG; Dong-xiao LI

doi:10.1631/jzus.C1300194

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K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps

Computer & Automation | Updated：2022-05-19

- K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps
- 基于K-最近邻域搜寻的ToF深度摄像机和被动立体深度获取的融合技术研究
- Frontiers of Information Technology & Electronic Engineering Vol. 15, Issue 3, Pages: 174-186(2014)
- Affiliations：
  
  1.Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China
  2.Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
- Author bio：
  
  [ "Li-wei LIU, lllw19870907@zju.edu.cn" ]
  [ "Yang LI, lychina@zju.edu.cn" ]
  Liang-hao WANG, wanglianghao@zju.edu.cn
- Funds：
- DOI：10.1631/jzus.C1300194
  CLC： TP317.4
- Received：15 July 2013，
  
  Revised：2014-2-19，
  
  Accepted：11 November 2013，
  
  Published：2014-03
- Accepted：
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Li-wei LIU, Yang LI, Ming ZHANG, et al. K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(3): 174-186.
DOI：

Li-wei LIU, Yang LI, Ming ZHANG, et al. K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(3): 174-186. DOI： 10.1631/jzus.C1300194.

摘要

研究目的

场景深度获取是目前三维显示研究领域中最关键的技术。目前主流的深度获取方式主要有两种：基于双目图像的被动立体匹配技术和基于主动光源测距的ToF深度摄像系统。两种方式获取的深度各有优缺点，本文旨在分析两者优缺点，融合两种方法获取的结果，形成质量更好的场景深度。

创新要点

利用ToF深度摄像机的优势区域指导立体匹配的过程，优化了立体匹配的结果，同时提出了一种新的代价优化深度融合算法，将ToF深度摄像机的采集结果和立体匹配产生的深度融合成精度更高的深度图。

研究方法

主要包含两部分算法，流程可见图1。首先，利用ToF深度摄像机提供的深度测量图和对应的光强振幅图构建能量函数，利用该能量函数，结合K-最近邻域算法，指导原始立体匹配过程。然后，将优化后的立体匹配结果和TOF深度图结合，构建代价函数，选取最优深度解，作为最终融合深度。

重要结论

实验结果显示，本文采用的算法获取的深度图优于单一主动或被动方法获取的深度图，也优于另一类全局优化的深度融合算法。

Abstract

Both time-of-flight (ToF) cameras and passive stereo can provide the depth information for their corresponding captured real scenes

but they have innate limitations. ToF cameras and passive stereo are intrinsically complementary for certain tasks. It is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. Although some fusion methods have been presented recently

they fail to consider ToF reliability detection and ToF based improvement of passive stereo. As a result

this study proposes an approach to integrating ToF cameras and passive stereo to obtain high-accuracy depth maps. The main contributions are: (1) An energy cost function is devised to use data from ToF cameras to boost the stereo matching of passive stereo; (2) A fusion method is used to combine the depth information from both ToF cameras and passive stereo to obtain high-accuracy depth maps. Experiments show that the proposed approach achieves improved results with high accuracy and robustness.

关键词

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references

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K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps

DOI：10.1631/jzus.C1300194

摘要

Abstract

关键词

Keywords

references