莫加深度:多级特征层次融合,用于轻量化单眼深度估计
1School of Information Engineering, Guangdong University of Technology, Guangzhou 510000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
MogaDepth通过专注于中级语义特征来增强单眼深度估计. 这种轻量级的架构可以提高真实世界应用的准确性和效率,例如自动驾驶.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 单眼深度估计对于自动驾驶和增强现实至关重要.
- 现有的轻量级方法往往忽略了关键的中阶语义特征相互作用.
研究的目的:
- 介绍MogaDepth,一种新的轻量级架构,用于改进单眼深度估计.
- 为了提高中层特征的表示,以获得更高的深度准确性.
主要方法:
- 开发了CMOGA模块 (Continuous Multi-Order Gated Aggregation) 模块,以增强中层级的功能.
- 引入了MambaSync,用于高效的全球-本地功能通信.
- 提出了MogaDepth,一种轻量级和富有表现力的网络架构.
主要成果:
- 在KITTI基准指标上,MogaDepth取得了竞争力或优异的表现,改善了错误指标.
- 在Make3D基准测试中表现优于现有方法,证明了对领域转移和具有挑战性的场景的稳定性.
- 在边缘设备上实现了高达13%的更快推断,而不会影响性能.
结论:
- 摩加深度为现实世界单眼深度估计提供了有效和高效的解决方案.
- 拟议的CMOGA和MambaSync模块大大有助于提高深度准确性和效率.
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