一个基于ROS的在线系统,用于3D高斯斯裂纹优化:灵活的前端集成和实时改进
Li'an Wang1, Jian Xu1, Xuan An1
1School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了使用ROS的在线3D高斯斯喷涂 (3DGS) 优化系统,显著减少了初始化时间并提高了场景重建质量. 这种新的方法提高了3DGS应用程序的实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 计算机图形 计算机图形
背景情况:
- 传统的结构-从-运动 (SfM) 方法用于3D高斯分片 (3DGS) 初始化是计算密集型,并遭受解的相机姿势优化和地图构造.
- 使用3DGS实时场景重建提供了效率,但面临着初始化瓶.
研究的目的:
- 建议和评估一个在线3DGS优化系统,利用ROS提高效率和集成.
- 解决传统基于SfM的3DGS初始化中的计算和脱问题.
主要方法:
- 开发了一个基于ROS的系统,具有松散合的架构,用于SfM/SLAM前端和3DGS后端之间的实时数据交互.
- 通过ROS中间件集成各种前端算法 (例如ORB-SLAM,COLMAP).
- 实现了动态滑动窗口策略和L1和SSIM染质量损失功能的组合,用于在线3DGS地图优化.
主要成果:
- 与传统的COLMAP-3DGS相比,减少了90%的3DGS初始化时间.
- 在TUM-RGBD,坦克和寺和KITTI数据集中实现了1.9dB的平均峰值信号噪声比 (PSNR) 改进.
- 证明了3DGS地图的有效在线优化,并提高了染质量.
结论:
- 拟议的基于ROS的在线3DGS优化系统显著提高了初始化速度和重建质量.
- 松散合的架构和优化的损失函数可以实现高效和强大的实时3D场景重建.
- 该系统为需要快速准确地绘制场景的3DGS应用提供了灵活高性能的替代方案.
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