Automatic Generation of Three Dimensional Colored Point Clouds Based on Multi-view Image Matching

ZHANG Ka; SHENG Ye-hua; MENG Wei-bo; XU Peng

doi:10.3788/OPE.20132107.1840

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Automatic Generation of Three Dimensional Colored Point Clouds Based on Multi-view Image Matching

更新时间：2020-08-12

- Automatic Generation of Three Dimensional Colored Point Clouds Based on Multi-view Image Matching
- Optics and Precision Engineering Vol. 21, Issue 7, Pages: 1840-1849(2013)
- 作者机构：
  
  1. 南京师范大学虚拟地理环境教育部重点实验室,江苏南京,210023
  2. 江苏兰德数码科技有限公司,江苏南京,210002
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132107.1840
  CLC： P231;TP751
- Received：18 December 2012，
  
  Revised：15 March 2013，
  
  Published Online：15 July 2013，
  
  Published：15 July 2013
- 稿件说明：
移动端阅览
张卡盛业华孟微波徐鹏. 基于多视影像匹配的三维彩色点云自动生成[J]. 光学精密工程, 2013,21(7): 1840-1849

. Automatic Generation of Three Dimensional Colored Point Clouds Based on Multi-view Image Matching[J]. Editorial Office of Optics and Precision Engineering, 2013,21(7): 1840-1849
张卡盛业华孟微波徐鹏. 基于多视影像匹配的三维彩色点云自动生成[J]. 光学精密工程, 2013,21(7): 1840-1849 DOI： 10.3788/OPE.20132107.1840.

. Automatic Generation of Three Dimensional Colored Point Clouds Based on Multi-view Image Matching[J]. Editorial Office of Optics and Precision Engineering, 2013,21(7): 1840-1849 DOI： 10.3788/OPE.20132107.1840.

摘要

针对三维虚拟场景构建的需要，提出了一种基于多视影像匹配的航空影像三维彩色点云自动生成算法。该算法采用了一种全新的物方与像方信息融合的多视影像概率松弛整体匹配策略，以综合利用多幅影像信息来提高匹配的可靠度；然后，基于多视影像匹配结果，采用多影像光束法平差来计算待匹配点的三维坐标；最后，采用平差模正确性的统计检验对三维坐标计算的准确度进行定量衡量。利用该算法对实际航空影像进行实验，统计检验得到的可靠度超过了90％，表明所提出的三维彩色点云生成方法具有较高的稳定度和精度。基于所提出的方法可将多视影像重叠区域内的所有平面像素转换为三维点，从而自动生成表达地理场景的全数字化的三维彩色点云，能很好地满足大范围三维地理场景快速重建的需要。

Abstract

With the aim to reconstruct three dimensional virtual scenes

a new algorithm for automatically generating three dimensional colored point clouds from aerial images is proposed in this paper. The algorithm adopts a new multi-view image probability relaxation global matching strategy based on fusion of object space and image space information to synthetically utilize multi-image information to improve matching reliability. Then

according to multi-view image matching results

multi-image bundle adjustment is used to compute the three dimensional coordinates of matching points. Finally

a statistical test on correctness of adjustment model is adopted to quantitatively measure the accuracy of computed three dimensional coordinates. The proposed algorithm is applied to actual aerial images. The experimental results on a statistical test indicate that matching reliability is above 90%

which means that the proposed algorithm has higher robustness and precision. Through proposed method

all planar pixels in the overlapping region of multi-view image are transformed to three dimensional points

and three dimensional colored point clouds with full digital format is automatically generated to represent geographic scenes. It can well satisfy the requirements of quick reconstruction of three dimensional geographic scenes with large scopes.

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references

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