Monocular Depth Estimation with Global-Aware Discretization and Local Context Modeling

2025-08-05

AI Summary

Monocular depth estimation suffers from inherent depth ambiguity due to the ill-posed nature of single-view projection. To address this, we propose a local–global collaborative modeling framework. Specifically, we design a Gated Large-Kernel Attention Module (GLKAM) that fuses multi-scale local structural cues via large-kernel convolutions and gated attention; additionally, we introduce a Global Bucket Prediction Module (GBPM) that explicitly models depth distribution priors to enforce global consistency. Joint optimization of these modules mitigates both local detail distortion and global structural misprediction. Our method achieves state-of-the-art performance on NYU-V2 and KITTI benchmarks, delivering significant improvements in accuracy and robustness. Experimental results validate the effectiveness of synergistically enhancing local contextual representation while guiding estimation with global prior knowledge.

Technology Category

• Computer Vision: Large Vision Models
• Search and Optimization: Local Search
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Monocular depth estimation from single-view ambiguity
• Improving accuracy with local and global cues
• Enhancing global perception via depth bin distribution

Innovation

• Gated Large Kernel Attention Module captures multi-scale local structures
• Global Bin Prediction Module enhances global depth perception
• Combines local and global cues for accurate depth estimation