﻿ 基于虚拟现实的船舶三维图像重建方法
 舰船科学技术  2023, Vol. 45 Issue (10): 172-175    DOI: 10.3404/j.issn.1672-7649.2023.10.035 PDF

Design of ship 3D image reconstruction method based on virtual reality
HE Yu-hai, CHEN Xiao-yan
Jiangxi University of Technology, Nanchang 330098, China
Abstract: By reconstructing and processing ship 3D images to improve the ability of ship dynamic detection and recognition, a virtual reality based ship 3D image reconstruction method is proposed. Based on the probability distribution of the spatial position of ship images, a subnet for ship 3D images is constructed in the feature map model is constructed. Virtual reality 3D reconstruction technology is used to achieve texture rendering and solid modeling in the process of ship 3D image reconstruction. Select the type of virtual reality reconstructed face in the Face Tools of the ship 3D image model, and achieve the reconstruction of the ship 3D image through dynamic rendering of color, transparency, and lighting effects. The experiment shows that this method has good visual expression ability and high precision level for ship 3D image reconstruction.
Key words: virtual reality     3D reconstruction     visual expression     texture rendering
0 引　言

1 船舶三维图模型分析和参数解析 1.1 船舶三维图像建模

 图 1 船舶三维图像重建实现结构图 Fig. 1 Implementation structure of ship 3D image reconstruction

 ${B_m}(x,y) = \frac{1}{m}\sum\limits_{i = 0}^{m - 1} {{F_i}(x,y)}。$ (1)

 ${E_{ext}}\left( \varphi \right) = \lambda {L_g}\left( \varphi \right) + v\left( I \right){A_g}\left( \varphi \right)。$ (2)

1.2 船舶三维纹理特征分析

 $y = \overline y + {{\boldsymbol{R}}_t}d，$ (3)
 $z = \overline z + {{\boldsymbol{R}}_h}d。$ (4)

 \begin{aligned} H = & \sqrt {{h_y}^2 + {h_z}^2 + {h_a}^2} = \\ & \sqrt {{{(g(y,z,y) \times {T_y})}^2} + {{(g(y,z,y) \times {T_z})}^2} + {{(g(y,z,y) \times {T_a})}^2}} 。\\ \end{aligned} (5)

$y_q^2 = im_q^2j$ 代入到邻域特征点匹配函数中，基于Hausdorff 距离参数估计的方法，得到船舶三维重建的最大相似度：

 $\mu （z,a,v)={\displaystyle \sum _{q=1}^{\infty }{c}_{q}}\mathrm{cos}\left[{y}_{q}v-\sqrt{\frac{{y}_{q}^{2}}{ij}}(z\mathrm{cos}{\rho }_{q})+{\phi }_{q}\right]。$ (6)

2 船舶三维图像重建 2.1 船舶三维特征虚拟现实重组

 $\bar z = {{ \bar z_0}} - \left(\frac{{ \bar m}}{m}\right)C\sin ( \bar m \cdot \bar z - yv)，$ (7)
 $a = C\cos ( \bar m\cdot {{ \bar z_0}} - yv)。$ (8)

 \left\{ {\begin{aligned} & {v_1 = \displaystyle\frac{M}{{E - Y}}Z} ，\\ & {w_1 = \displaystyle\frac{M}{{E - Y}}A} ，\\ & {v_2 = \displaystyle\frac{M}{{E - (Y\cos \vartheta + Z\sin \vartheta )}}(Z\cos \vartheta - Y\sin \vartheta )} ，\\ & {v_2 = \displaystyle\frac{L}{{D - (X\cos \theta + Y\sin \theta )}}Z}。\end{aligned}} \right. (9)

2.2 船舶三维图像动态重建实现

 ${l_w} = \displaystyle\frac{{{l_s} + {l_f} + {v_f} \cdot {v^2}}}{{20}}。$ (10)

 图 2 船舶三维重建参数关系 Fig. 2 Relationship between ship 3D reconstruction parameters
3 仿真测试

 图 3 船舶三维体绘制 Fig. 3 3D drawing of ships

 图 4 船舶三维重建结果 Fig. 4 3D reconstruction results of ships

 图 5 重建性能对比 Fig. 5 Comparison of reconstruction performance
4 结　语

 [1] 李松卿, 丁刚毅. 基于成像逼真度的视觉仿真方法[J]. 中国电子科学研究院学报, 2021, 16(1): 14-20. LI Song-qing, DING Gang-yi. Visual simulation method based on imaging fidelity[J]. Journal of China Academy of Electronic Sciences, 2021, 16(1): 14-20. DOI:10.3969/j.issn.1673-5692.2021.01.003 [2] 陈瑶, 陈思. 基于自适应多普勒及动态邻域的改进BA算法[J]. 计算机工程与应用, 2021, 57(22): 166-176. CHEN Yao, CHEN Si. Improved BA algorithm based on adaptive Doppler and dynamic neighborhood[J]. Computer Engineering and Applications, 2021, 57(22): 166-176. [3] 张颢, 孟祥伟, 李德胜, 等. 基于局部窗口K分布的快速舰船检测算法[J]. 计算机应用, 2016, 36(3): 859-863. ZHANG Hao, MENG Xiang-wei, LI De-sheng, et al. A fast ship detection algorithm based on local window K distribution[J]. Computer Applications, 2016, 36(3): 859-863. [4] 王培元, 周建军, 王日胜, 等. 基于多尺度自适应显著区域检测的舰船三维重建外点消除[J]. 河北师范大学学报(自然科学版), 2017, 41(6): 477-483. WANG Pei-yuan, ZHOU Jian-jun, WANG Ri-sheng, et al. External point elimination in ship 3D reconstruction based on multi-scale adaptive salient region detection[J]. Journal of Hebei Normal University (Natural Science Edition), 2017, 41(6): 477-483. DOI:10.13763/j.cnki.jhebnu.nse.2017.06.004 [5] 汤子麟, 刘翔, 张星. 光照不均匀图像的自适应增强算法[J]. 计算机工程与应用, 2021, 57(21): 216-223. TANG Zi-lin, LIU Xiang, ZHANG Xing. Adaptive enhancement algorithm for uneven illumination images[J]. Computer Engineering and Applications, 2021, 57(21): 216-223.