Optimizing Monocular Depth Estimation through Bi-Level Nested Architecture Integration

Abstract

Monocular depth estimation (MDE) has evolved into a vital research focus in visual computing and autonomous robotic systems. While recent deep learning approaches have advanced the field, existing methods often struggle to jointly incorporate global context and fine-grained local details, and may be hampered by complex, computationally expensive network architectures. To address these challenges, we employ the U<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net framework, which incorporates Residual U-Blocks designed to adapt the area of reception and capture multi-scale contextual data. By nesting U-shaped structures, U<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net effectively increases feature extraction depth while maintaining computational efficiency, thus offering a more balanced and scalable solution for MDE. Our proposed model, developed using the NYUDepth V2 dataset, exceeds contemporary methods in critical assessment measures, including RMSE, RMSE(log), and Absolute Relative Error (Abs Rel). These improved results highlight the robustness and precision of proposed model or a range of practical applications in robotics, augmented reality, and other domains requiring real-time scene geometry understanding.