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High Precision 3D Scene Reconstruction Based on Monocular Vision
DOI: 10.12677/AIRR.2018.73013, PDF, HTML, XML, 下载: 1,067  浏览: 3,518  科研立项经费支持

Abstract: In recent years, along with the rapid updating of computer hardware, the processing capability of computer is also increasing. At the same time, 3d scene reconstruction technique has become more and more mature and we can get 3d model data for scenarios more easily than ever before. Now, in the 3d reconstruction technology based on monocular and binocular, monocular technology is simpler to operate than binocular technology and more convenient to acquire materials and more favorable to the market. This paper focuses on monocular based 3D reconstruction, the algorithm is used to reconstruct the 3d scene with a fast NCC algorithm based on the cumulative diagram. This paper improves the classic NCC similarity measures to reduce the computation time. Seed pixel expansion algorithm is presented to choose the initial seed pixels, use parallax to make window comparisons to obtain high confidence seed pixels, therefore, the mismatches of the parallax figure are greatly reduced. Experiments show that the method can reconstruct precise and clear 3d scenarios.

1. 引言

2. 相关工作

3. 场景高精度三维模型重建算法

3.1. 算法概述

3.2. 摄像机内参数标定

Figure 1. Algorithm flow diagram

3.3. 图像采集和特征匹配

3.4. 图像间相对位置计算

3.5. 图像校正

Figure 2. The diagrams used in the experiment

Figure 3. The result of SIFT feature matching of two corrected images

3.6. 立体匹配

3.6.1. 基于累积图的快速NCC匹配代价计算

NCC匹配代价为：

$C\left(p,d\right)=\frac{\underset{\left(x,y\right)\in {W}_{p}}{\sum }\left({I}_{1}\left(x,y\right)-{\stackrel{¯}{I}}_{1}\left({p}_{x},{p}_{y}\right)\right)\cdot \left({I}_{2}\left(x+d,y\right)-{\stackrel{¯}{I}}_{2}\left({p}_{x}+d,{p}_{y}\right)\right)}{\sqrt{\underset{\left(x,y\right)\in {W}_{p}}{\sum }{\left({I}_{1}\left(x,y\right)-{\stackrel{¯}{I}}_{1}\left({p}_{x},{p}_{y}\right)\right)}^{2}\cdot \underset{\left(x,y\right)\in {W}_{p}}{\sum }{\left({I}_{2}\left(x+d,y\right)-{\stackrel{¯}{I}}_{2}\left({p}_{x}+d,{p}_{y}\right)\right)}^{2}}}$ (1)

$i{i}_{k}\left({W}_{p}\right)=\underset{\left(x,y\right)\in {W}_{p}}{\sum }{I}_{k}\left(x,y\right)$ (2)

${I}_{22}\left(x,y\right)={I}_{2}\left(x,y\right)×{I}_{2}\left(x,y\right)$ (3)

${D}_{r}={D}_{\mathrm{max}}-{D}_{\mathrm{min}}+1$ (4)

${I}_{12}\left(x,y\right)={I}_{1}\left(x,y\right)×{I}_{2}\left(x+d,y\right)$ (5)

$C\left(p,d\right)=\frac{i{i}_{12}\left({W}_{p}\right)-\frac{1}{|{W}_{p}|}\cdot i{i}_{1}\left({W}_{p}\right)\cdot i{i}_{2}\left({W}_{p+d}\right)}{\sqrt{\left[i{i}_{11}\left({W}_{p}\right)-\frac{1}{|{W}_{p}|}\cdot i{i}_{1}{\left({W}_{p}\right)}^{2}\right]\cdot \left[i{i}_{22}\left({W}_{p+d}\right)-\frac{1}{|{W}_{p}|}\cdot i{i}_{2}{\left({W}_{p+d}\right)}^{2}\right]}}$ (6)

$O\left(WH\right)+O\left(WH\right)+\left(O\left(WH\right)+O\left(WH\right)\right)+\left(O\left(WH\right)+O\left(WH\right)\right)=O\left(WH\right)$ (7)

3.6.2. 种子像素提取算法

$\left\{\begin{array}{l}k×C\left({p}_{x},{p}_{y},disp\right)\ge C\left({p}_{x},{p}_{y},d\right),\forall d\ne disp\\ k×C\left({p}_{x},{p}_{y},disp\right)\ge C\left({{p}^{\prime }}_{x},{p}_{y},{d}^{\prime }\right),{{p}^{\prime }}_{x}+{d}^{\prime }={p}_{x}+disp,{d}^{\prime }\ne disp\end{array}$ (8)

${S}_{u}=\left\{\left({p}_{x},{p}_{y},disp\right)|{p}_{y}<\frac{H}{2}\right\}$ (9)

${S}_{d}=\left\{\left({p}_{x},{p}_{y},disp\right)|{p}_{y}\ge \frac{H}{2}\right\}$ (10)

$\underset{i={p}_{x}-w}{\overset{{p}_{x}+w}{\sum }}\underset{j={p}_{y}-w}{\overset{{p}_{y}+w}{\sum }}{\left({D}_{u}\left(i,j\right)-{D}_{d}\left(i,j\right)\right)}^{2}=0$ (11)

$P\left({p}_{x},{p}_{y},d\right)\propto C\left({p}_{x},{p}_{y},d\right)$ (12)

$\begin{array}{l}{d}^{\prime }={\mathrm{arg}}_{disp}\mathrm{max}\left(C\left({{p}^{\prime }}_{x},{{p}^{\prime }}_{y},disp\right)\right)\\ disp\in \left\{d-1,d,d+1\right\}\end{array}$ (13)

$\begin{array}{l}{d}^{\prime }={\mathrm{arg}}_{disp}\mathrm{max}\left(C\left({{p}^{\prime }}_{x},{{p}^{\prime }}_{y},disp\right)\right)\\ disp\in \left\{d-1,d,d+1,{d}_{1}\right\}\end{array}$ (14)

4. 实验结果

Figure 4. Comparison of computation time of NCC matching cost method. (a) Match the operation time different windows sizes. (b) Comparison of only NCC matching cost calculation is analyzed

5. 重建结果

Figure 5. Reconstruction results of NO. 1 indoor scene

Figure 6. Reconstruction results of NO. 2 indoor scene

6. 结论

NOTES

*通讯作者。

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