面向无人机路径规划的可视分析系统

doi:10.11996/JG.j.2095-302X.2025030655

图学学报 ›› 2025, Vol. 46 ›› Issue (3): 655-665.DOI: 10.11996/JG.j.2095-302X.2025030655

• 计算机图形学与虚拟现实 • 上一篇下一篇

面向无人机路径规划的可视分析系统

胡悦(), 孙智达, 黄惠()

深圳大学计算机与软件学院可视计算研究中心，广东深圳 518060

收稿日期:2024-07-11 接受日期:2024-10-08 出版日期:2025-06-30 发布日期:2025-06-13
通讯作者:黄惠(1977-)，女，教授，博士。主要研究方向为计算机图形学。E-mail：huihuang@szu.edu.cn
第一作者:胡悦(2000-)，男，硕士研究生。主要研究方向为计算机图形学。E-mail：hytraveler2000@gmail.com
基金资助:
国家自然科学联合基金重点项目(U21B2023);广东高等学校创新团队项目(2022KCXTD025);深圳大学教学改革重点项目(JG2024018);卓越研究计划项目(2035)

Visual analysis system for UAV path planning

HU Yue(), SUN Zhida, HUANG Hui()

Visual Computing Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen Guangdong 518060, China

Received:2024-07-11 Accepted:2024-10-08 Published:2025-06-30 Online:2025-06-13
Contact: HUANG Hui (1977-), professor, Ph.D. Her main research interest covers computer graphics. E-mail：huihuang@szu.edu.cn
First author：HU Yue (2000-), master student. His main research interest covers computer graphics. E-mail：hytraveler2000@gmail.com
Supported by:
National Natural Science Foundation Key Project(U21B2023);Guangdong University Innovation Team Project(2022KCXTD025);Shenzhen University Key Teaching Reform Project(JG2024018);Research Excellence Program Project(2035)

摘要/Abstract

摘要：

目前，利用真实世界的图像信息进行几何模型重建，并采用基于图像的渲染方法重建出高质量渲染结果成为一种获取高质量美术素材的解决方案。为此，提出了一个可视分析系统为无人机规划路径以及与之相关的三维重建和基于图像渲染提供支持。工程贡献上以蓝图编辑器作为可视分析系统的程序载体，将各种可视分析功能编写为蓝图编辑器中的功能节点，允许用户通过拖拽、连接和配置图形元素，自定义地创建用户所需的可视分析工作流。算法创新上提出了一种对基于采样覆盖率的参考视角选择算法的改进优化方案，比较了其他3种类型的视角选择算法后，通过对比实验证实了参考视角选择方案在运算的时间开销上和渲染图像的参考率上拥有更好的效果。

关键词: 数据可视化, 无人机, 三维重建, 基于图像的渲染, 可视分析, 路径规划

Abstract:

Currently, real-world image information have been used to reconstruct geometric models and generate high-quality rendered outcomes using image-based rendering methods, which has become a viable solution for acquiring high-quality art materials. A visual analysis system was designed to support UAV path planning, 3D reconstruction, and image-based rendering. In terms of engineering contributions, a blueprint editor was employed as the platform for this visual analysis system, wherein various visual analysis functions were implemented as function nodes within the editor, thereby enabling users to personalize their visual analysis workflow by simply dragging, connecting, and configuring graphic elements. In terms of algorithm innovation, an enhanced and optimized view selection algorithm based on sampling coverage was proposed. This algorithm offered improved results in terms of both algorithmic time cost and rendering coverage.

Key words: data visualization, unmanned aerial vehicles, 3D reconstruction, image-based render, visual analysis, path planning

中图分类号:

TP391
V279

胡悦, 孙智达, 黄惠. 面向无人机路径规划的可视分析系统[J]. 图学学报, 2025, 46(3): 655-665.

HU Yue, SUN Zhida, HUANG Hui. Visual analysis system for UAV path planning[J]. Journal of Graphics, 2025, 46(3): 655-665.

图/表 14

图1 无人机采集、三维重建与基于图像渲染的关系

Fig. 1 The relationship among drone data acquisition, 3D reconstruction, and image-based rendering

图2 基于图像渲染的整体画面质量会被无参考像素影响

Fig. 2 The overall picture quality based on image rendering is affected by the absence of reference pixels

图3 用户可以自行添加功能节点完成任意的可视分析任务

Fig. 3 Users can add functional nodes to perform any visual analysis task

图4 EDL和SSAO渲染效果对比

Fig. 4 EDL and SSAO render effect comparison ((a) EDL; (b) SSAO)

图5 可视分析系统中渲染流水线

Fig. 5 Rendering pipeline in visual analysis system

图6 在三维重建可视分析流程中相关需求(R)对应实现的目标(T)以及不同需求间的内在逻辑

Fig. 6 In process of visual analysis of the 3D reconstruction, the relevant requirements (R) correspond to the goal (T) to be achieved, and the internal logic between the different requirements

图7 在IBR渲染质量可视分析流程中相关需求(R)对应实现的目标(T)以及不同需求间的内在逻辑

Fig. 7 In process of the visual analysis of IBR render quality, the related requirements (R) correspond to the objectives (T) achieved, and the internal logic between the different requirements

图8 对点云的预处理可以增强视角的选择效率((a)贪婪法无法保证选到最优参考视角组合；(b)优化后的点云作为输入可以辅助算法选择到更好的参考视角组合)

Fig. 8 The preprocessing of point clouds can enhance the selection effect of perspectives ((a) Greedy method can not guarantee the selection of reference view combination is good enough; (b) The optimized point cloud as input can assist the algorithm to select a better reference view combination)

图9 使用渲染过程中的无参考像素优化点云

Fig. 9 Use unreferenced pixels in the rendering process to optimize the point cloud

图10 对无人机每个拍摄点位计算其相对表面点云在输入场景下的是否可见便可得到可见性位图

Fig. 10 The visibility bitmap can be obtained by calculating whether the surface point cloud is visible in the input scene at each shooting point of the UAV

图11 案例分析流程图

Fig. 11 Case studies processes

表1 对比实验结果

Table 1 Comparative experimental results

优化方案	场景类别	点云规模/万	平均可见性计算开销/ms	优/%	良/%	差/%	平均像素遮挡率/%
无优化	学校	10	5.4	76.18	22.09	1.73	0.111
无优化	小镇	10	7.3	88.92	10.89	0.18	0.043
可见性非精确计算	学校	100	1.1	79.43	19.81	0.77	0.082
可见性非精确计算	小镇	255	1.2	93.18	6.74	0.08	0.029
非均匀点云优化	学校	100	1.1	81.61	18.01	0.38	0.068
非均匀点云优化	小镇	255	1.2	93.70	6.22	0.08	0.029
采集视角补充	学校	100	1.1	86.04	13.80	0.16	0.046
采集视角补充	小镇	255	1.2	93.72	6.22	0.06	0.026

图12 不同方案优化后的渲染案例展示

Fig. 12 The rendering case of different schemes is optimized

表2 与其他工作的对比

Table 2 Results compared with other work

方法	平均视角选择时间开销/ms	是否支持稀疏参考视角集	平均像素遮挡率/%
文献[14]	0.012	否	/
文献[16]	8.900	是	1.410
文献[19]	10.400	是	0.111
Ours	6.200	是	0.068

参考文献 22

[1]	SHUM H, KANG S B. Review of image-based rendering techniques[EB/OL]. [2024-05-11]https://www.spiedigitallibrary.org/conference-proceedings-of-spie/4067/1/Review-of-image-based-rendering-techniques/10.1117/12.386541.short.
[2]	SCHMID K, HIRSCHMÜLLER H, DÖMEL A, et al. View planning for multi-view stereo 3D reconstruction using an autonomous multicopter[J]. Journal of Intelligent & Robotic Systems, 2012, 65(1/4): 309-323.
[3]	ZHANG H, YAO Y C, XIE K, et al. Continuous aerial path planning for 3D urban scene reconstruction[J]. ACM Transactions on Graphics, 2021, 40(6): 225.
[4]	ZHOU X H, XIE K, HUANG K, et al. Offsite aerial path planning for efficient urban scene reconstruction[J]. ACM Transactions on Graphics, 2020, 39(6): 192.
[5]	SMITH N, MOEHRLE N, GOESELE M, et al. Aerial path planning for urban scene reconstruction: a continuous optimization method and benchmark[J]. ACM Transactions on Graphics, 2018, 37(6): 183.
[6]	KNAPITSCH A, PARK J, ZHOU Q Y, et al. Tanks and temples: benchmarking large-scale scene reconstruction[J]. ACM Transactions on Graphics, 2017, 36(4): 78.
[7]	SEITZ S M, CURLESS B, DIEBEL J, et al. A comparison and evaluation of multi-view stereo reconstruction algorithms[C]// 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2006: 519-528.
[8]	LIU Y L, LIN L Q, HU Y, et al. Learning reconstructability for drone aerial path planning[J]. ACM Transactions on Graphics, 2022, 41(6): 197.
[9]	LEVOY M, HANRAHAN P. Light field rendering[C]// The 23rd Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 1996: 31-42.
[10]	GORTLER S J, GRZESZCZUK R, SZELISKI R, et al. The lumigraph[C]// The 23rd Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 1996: 43-54.
[11]	DEBEVEC P E, TAYLOR C J, MALIK J. Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach[C]// The 23rd Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 1996: 11-20.
[12]	BUEHLER C, BOSSE M, MCMILLAN L, et al. Unstructured lumigraph rendering[C]// The 28th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 2001: 425-432.
[13]	HEDMAN P, RITSCHEL T, DRETTAKIS G, et al. Scalable inside-out image-based rendering[J]. ACM Transactions on Graphics, 2016, 35(6): 231.
[14]	XU J M, WU X C, ZHU Z H, et al. Scalable image-based indoor scene rendering with reflections[J]. ACM Transactions on Graphics, 2021, 40(4): 60.
[15]	HEDMAN P, PHILIP J, PRICE T, et al. Deep blending for free-viewpoint image-based rendering[J]. ACM Transactions on Graphics, 2018, 37(6): 257.
[16]	RIEGLER G, KOLTUN V. Free view synthesis[C]// The 16th European Conference on Computer Vision. Cham: Springer, 2020: 623-640.
[17]	RIEGLER G, KOLTUN V. Stable view synthesis[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 12211-12220.
[18]	PARK J J, FLORENCE P, STRAUB J, et al. DeepSDF: learning continuous signed distance functions for shape representation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 165-174.
[19]	YI Z M, XIE K, LYU J H, et al. Where to render: studying renderability for IBR of large-scale scenes[C]// 2023 IEEE Conference on Virtual Reality and 3D User Interfaces. New York: IEEE Press, 2023: 356-366.
[20]	BOUCHENY C. Interactive scientific visualization of large datasets: towards a perceptive-based approach[D]. Grenoble: Université Joseph Fourier, 2009.
[21]	BAVOIL L, SAINZ M. Screen space ambient occlusion[EB/OL]. (2008-09-01) [2024-10-03]https://developper.download.nvidia.com/SDK/10.5/direct3d/Source/ScreenSpaceAO/doc/ScreenSpaceAO.pdf.
[22]	LIN L Q, LIU Y L, HU Y, et al. Capturing, reconstructing, and simulating: the UrbanScene3D dataset[C]// The 17th European Conference on Computer Vision. Cham: Springer, 2022: 93-109.

面向无人机路径规划的可视分析系统

Visual analysis system for UAV path planning

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王道累, 丁子健, 杨君, 郑劭恺, 朱瑞, 赵文彬. 基于体素网格特征的NeRF大场景重建方法[J]. 图学学报, 2025, 46(3): 502-509.
[2]	黄志勇, 佘雅丽, 华喜锋, 向梦丽, 杨晨龙, 丁妥君. DCSplat：一种深度约束的稀疏视角三维重建方法[J]. 图学学报, 2025, 46(3): 510-519.
[3]	孙禾衣, 李艺潇, 田希, 张松海. 结合程序内容生成与扩散模型的图像到三维瓷瓶生成技术[J]. 图学学报, 2025, 46(2): 332-344.
[4]	周伟, 苍慜楠, 程浩宗. 基于AR技术的文物数字化三维图像重建方法[J]. 图学学报, 2025, 46(2): 369-381.
[5]	邱佳新, 宋倩云, 徐丹. 基于改进神经辐射场的民族舞蹈重建方法[J]. 图学学报, 2025, 46(2): 415-424.
[6]	李琼, 考月英, 张莹, 徐沛. 面向无人机航拍图像的目标检测研究综述[J]. 图学学报, 2024, 45(6): 1145-1164.
[7]	宋思程, 陈辰, 李晨辉, 王长波. 基于密度图多目标追踪的时空数据可视化[J]. 图学学报, 2024, 45(6): 1289-1300.
[8]	闫建红, 冉同霄. 基于YOLOv8的轻量化无人机图像目标检测算法[J]. 图学学报, 2024, 45(6): 1328-1337.
[9]	李刚, 蔡泽浩, 孙华勋, 赵振兵. 基于改进YOLOv8与语义知识融合的金具缺陷检测方法研究[J]. 图学学报, 2024, 45(5): 979-986.
[10]	熊超, 王云艳, 罗雨浩. 特征对齐与上下文引导的多视图三维重建[J]. 图学学报, 2024, 45(5): 1008-1016.
[11]	贾明超, 冯斌, 吴鹏, 张坤, 桑胜举. 一种融合改进A*算法与改进动态窗口法的文旅服务机器人路径规划[J]. 图学学报, 2024, 45(3): 505-515.
[12]	卢元杰, 陈星伊, 苏大林, 孙唯. 面向系统工程的无人机自主定位系统研究[J]. 图学学报, 2024, 45(2): 363-368.
[13]	黄家晖, 穆太江. 动态三维场景重建研究综述[J]. 图学学报, 2024, 45(1): 14-25.
[14]	石敏, 王炳祺, 李兆歆, 朱登明. 一种带高光处理的无缝纹理映射方法[J]. 图学学报, 2024, 45(1): 148-158.
[15]	周婧怡, 张栖桐, 冯结青. 基于混合结构的多视图三维场景重建[J]. 图学学报, 2024, 45(1): 199-208.