在线场景语义理解基于AR的稀疏相关网络
Qianqian Wang1, Junhao Song1, Chenxi Du1
1The School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 102488, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种用于实时RGBD实例分割的新型稀疏相关网络 (SCN),通过利用对相关性和稀疏数据来提高准确性和一致性,以实现虚拟-真实交互.
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
- 人工智能的人工智能
背景情况:
- 基于二维图像的场景理解缺乏几何细节,并与阻塞作斗争.
- 将深度传感器与几何和语义信息集成,带来了融合挑战.
- 现实世界的理解对于虚拟现实映射和交互至关重要.
研究的目的:
- 开发一种在线RGBD实例细分方法,克服现有方法的局限性.
- 通过从RGBD数据中有效地融合几何和语义信息来增强场景理解.
- 为增强现实等应用程序提供强大的现实世界的理解.
主要方法:
- 引入了用于在线RGBD实例分割的稀相关网络架构 (SCN).
- 利用对象级RGB-D SLAM系统和对时间相关性.
- 利用稀疏的数据生成和对象布局先验来减少复杂性和提高效率.
主要成果:
- 与最先进的方法相比,在实例细分方面取得了显著的准确性和一致性.
- 使用稀疏数据,以每秒18的处理速度展示了实时性能.
- 在NYU Depth V2和ScanNet V2数据集上验证了性能.
结论:
- 视频流中的对相关性提高了RGBD实例细分的准确性和一致性.
- 稀疏的数据处理保持了实时性能,同时减少了计算复杂性.
- 拟议的方法显示了通过对象布局理解增强现实应用的实际潜力.
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