用多个平衡基点和密集单眼SLAM的信心来完成深度完成.
IEEE transactions on visualization and computer graphics
|July 29, 2024
概括
本研究介绍了BBC-Net,这是一个轻量级的网络,用于在移动设备上增强/虚拟现实 (AR/VR) 中单眼密集映射. 它为实时应用程序提供高效,高质量的密集SLAM (同时定位和映射).
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
- 增强现实和虚拟现实.
背景情况:
- 单眼密集的同时定位和映射 (SLAM) 对移动设备上的AR/VR至关重要.
- 现有的稀疏的SLAM系统缺乏密集的映射功能,限制了AR/VR应用.
- 移动平台上的实时密集映射仍然是一个重大挑战.
研究的目的:
- 开发一种新的方法,在移动设备上的稀疏SLAM系统中实现密集映射.
- 引入一个轻量级的深度完成网络,BBC-Net,优化用于稀疏的SLAM集成.
- 为了提高单眼SLAM对AR/VR应用的效率和映射质量.
主要方法:
- 将轻量级深度完成网络 (BBC-Net) 集成到稀疏的SLAM系统中.
- 使用多基底深度表示与预测的基础和信心地图.
- 设计深度重量因子,以便在SLAM捆绑调整中无集成和优化.
主要成果:
- 从单眼图像中,BBC-Net成功地预测了多个深度基地和信心地图.
- 与最先进的技术相比,拟议的方法实现了优越的单眼密度映射性能.
- 在手机上进行的在线演示验证了该方法在现实场景中的效率和映射质量.
结论:
- 拟议的BBC-Net提供了一个多功能插件模块,用于增强现有的稀疏SLAM系统.
- 该方法显著改善了全球深度的一致性,并使手机上的实时密集映射成为可能.
- 这一进步对于需要准确的3D场景重建的AR/VR应用具有实质价值.
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