通过全局分布与局部结构结合，提升单目深度估计精度。
Monocular Depth Estimation with Global-Aware Discretization and Local Context Modeling
- 用门控大卷积核捕捉多尺度局部结构信息
- 通过全局深度箱分布预测提升整体结构感知
- 在NYU-V2和KITTI上优于现有方法
由于从单视角恢复3D结构存在固有歧义，单目深度估计仍是难题——多个合理的深度配置可能产生相同的二维投影。本文提出一种新方法，融合局部与全局线索以提高预测精度。具体而言，设计了门控大卷积核注意力模块（GLKAM），利用带门控机制的大卷积核有效捕获多尺度局部结构信息；为增强网络的全局感知能力，引入全局箱预测模块（GBPM），估计深度箱的全局分布，并为深度回归提供结构引导。在NYU-V2和KITTI数据集上的大量实验表明，该方法性能具有竞争力，且优于现有方法，验证了各组件的有效性。
原文摘要 · Abstract (English)
Accurate monocular depth estimation remains a challenging problem due to the inherent ambiguity that stems from the ill-posed nature of recovering 3D structure from a single view, where multiple plausible depth configurations can produce identical 2D projections. In this paper, we present a novel depth estimation method that combines both local and global cues to improve prediction accuracy. Specifically, we propose the Gated Large Kernel Attention Module (GLKAM) to effectively capture multi-scale local structural information by leveraging large kernel convolutions with a gated mechanism. To further enhance the global perception of the network, we introduce the Global Bin Prediction Module (GBPM), which estimates the global distribution of depth bins and provides structural guidance for depth regression. Extensive experiments on the NYU-V2 and KITTI dataset demonstrate that our method achieves competitive performance and outperforms existing approaches, validating the effectiveness of each proposed component.
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