Efficient Monocular Depth-Based Physical Distance Measurement for Low-Depth Scales

In this study, we introduce the physical distance estimator (PDE), a comprehensive framework designed to precisely measure the physical distance between two points on objects with a low-depth scale. The PDE utilizes monocular depth estimation to reconstruct 2-D depth maps into 3-D point clouds and calculates the physical distance between two points using the 3-D Euclidean distance. To achieve precise measurements for objects with low-depth scales, we configure a novel dataset using an RGB-Depth (RGB-D) camera to capture both RGB images and their depth maps of objects within low-depth scales, simulating various environmental conditions. We obtain a metric depth map by fine-tuning the monocular depth estimation model with the dataset. Furthermore, we applied knowledge distillation and FP16 optimization techniques to reduce the computational cost of PDE while maintaining high accuracy. These optimizations ensure that PDE operates efficiently in resource-constrained industrial environments, enabling real-time performance. Our experimental results show that PDE achieves an mean squared error of 29.60 and an mean absolute error of 3.17, representing improvements of 13.1% and 9.1%, respectively, over the NYUv2 benchmark. These quantitative results highlight the effectiveness and reliability of our method in real-world industrial settings.