室内物体通过冗余和比较方法进行测量.
Pedro Faria1, Tomás Simões2, Tiago Marques3
1Infrastructure Department, Hainan University, Haikou 570228, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究提出了一个新的计算机视觉框架,用于使用标准智能手机摄像头进行准确的室内空间测量. 它克服了以前方法的局限性,通过利用几何和建筑规则来精确地估计墙壁和空间尺寸.
科学领域:
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
- 室内空间测量室内空间测量
背景情况:
- 传统的计算机视觉方法在室内环境中难以准确地检测和测量物体,特别是在视觉参考有限的极简空间.
- 现有的模型往往无法产生完整的检测或准确的距离估计,例如超过摄像机视野的墙壁等元素.
研究的目的:
- 引入一种新的以几何驱动的,基于冗余的框架,以提高室内环境中的墙壁和空间划分的测量精度.
- 为了使准确的空间测量只使用标准的智能手机摄像头,没有专门的硬件.
主要方法:
- 开发了一个利用比例规律和架构启发式测试来提高测量的框架.
- 在204张标记的室内图像上训练模型,在500个时代中进行数据增强.
- 实施冗余校正方法以减少距离偏差错误.
主要成果:
- 实现了0.995的平均平均精度 (mAP@50),0.995的精度和0.992的回忆,表明模型的融合和概括.
- 冗余校正方法将距离偏差误差减少到大约10%,平均绝对误差在使用案例中低于2%.
- 该解决方案运行在2D视觉输入上,允许在设备上和离线使用,无需专门的硬件.
结论:
- 拟议的框架为使用标准智能手机摄像头进行准确的室内空间测量提供了一个可扩展,低成本的替代方案.
- 在现实室内环境中展示了基于相机的几何校正的可行性.
- 未来的工作可能会将这种方法与多式模式模型集成在一起,用于各种应用中的全房间空间推理.
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