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在光场估计与成本量构建和聚合方面的效率-精度权衡
Bo Xiao1,2, Stuart Perry2, Xiujing Gao3,4
1State Key Laboratory of Advanced Design and Manufacturing for the Vehicle Body, Hunan University, Changsha 410012, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种轻量级的光场深度估计模型,可以提高在无纹理区域等具有挑战性的区域的准确性. 它在不牺牲深度准确性的情况下实现了高计算效率.
科学领域:
- 计算机视觉 计算机视觉
- 这是一种摄影计量技术 (photogrammetry).
背景情况:
- 光场图像提供丰富的空间和角度数据用于深度估计,对于环境感知至关重要.
- 高的计算成本和内存需求往往限制了光场深度估计的实际应用.
- 修剪光场数据提高了效率,但降低了准确性,特别是在低质感或封闭区域.
研究的目的:
- 开发用于光场图像的轻量差异估计模型.
- 提高深度估计的准确性,特别是在无纹理和封闭区域.
- 为了平衡计算效率与高精度深度估计.
主要方法:
- 结合绝对差异和相关性成本匹配,以建立强大的成本量.
- 开发了一种使用3D卷积和类似UNet的结构的多尺度差异成本融合架构.
- 跨多个深度尺度的综合信息,以实现高效的成本,体积融合和完成.
主要成果:
- 实现了与最先进的高效方法可比的计算效率.
- 与现有的高效光场深度估计技术相比,准确度翻了一番.
- 达到了与最高准确度的方法相似的准确度水平,并显著提高了计算性能.
结论:
- 拟议的轻量级模型有效地提高了在具有挑战性的地区的深度估计准确性.
- 该方法为光场深度估计提供了速度和准确性之间的卓越平衡.
- 这种方法为使用光场的实时环境感知提供了一个实际的解决方案.
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