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RepACNet:用于单眼深度估计的轻量级重复参数化非对称卷积网络
Wanting Jiang1, Jun Li1, Yaoqian Niu1
1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
RepACNet为单眼深度估计 (MDE) 提供了一个轻量级的解决方案,为移动设备平衡效率和准确性. 这种新型网络使用重新参数化的不对称卷积和MLP-Mixer组件进行有效的2D/3D场景重建.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 3D场景重建 3D场景重建
背景情况:
- 单眼深度估计 (MDE) 对于2D/3D场景重建至关重要,在自动驾驶和机器人技术中具有应用.
- 当前的MDE方法在计算效率和准确性之间的权衡中扎,阻碍了对资源有限的设备的部署.
- 对于移动应用程序,需要轻量级但有效的MDE模型.
研究的目的:
- 开发一个新的轻量级网络,RepACNet,以实现高效和准确的单眼深度估计.
- 在计算成本和性能方面解决现有的MDE方法的局限性.
- 为了使MDE能够在资源有限的移动设备上部署.
主要方法:
- RepACNet集成了基于CNN的架构与MLP-Mixer组件.
- 引入了具有非对称卷积的重构标记混合器 (RepTMAC),以实现高效的长距离依赖性捕获与线性复杂性.
- 集成的压缩和刺激连续扩展卷积 (SECDCs) 用于使用通道注意力进行多尺度深度特征提取.
主要成果:
- 在NYU Depth v2和KITTI Eigen基准指标上,RepACNet取得了竞争性表现.
- 与最先进的MDE方法相比,拟议的模型保持的参数显著较少.
- RepTMAC能够在最小的计算开销下实现全球功能交互,优于基于变压器的方法.
结论:
- RepACNet为单眼深度估计提供了一种有效的轻量化解决方案.
- 网络设计成功地平衡了计算效率和估计准确度.
- RepACNet适合在资源有限的移动设备上部署,从而推进计算机视觉中的应用.
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