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.

Beyond the Transformer, it is important to explore how to exploit the capacity of the MetaFormer, an architecture that is fundamental to the performance improvements of the Transformer. Previous studies have exploited it only for the backbone network. Unlike previous studies, we explore the capacity of the Metaformer architecture more extensively in the semantic segmentation task. We propose a powerful semantic segmentation network, MetaSeg, which leverages the Metaformer architecture from the backbone to the decoder. Our MetaSeg shows that the MetaFormer architecture plays a significant role in capturing the useful contexts for the decoder as well as for the backbone. In addition, recent segmentation methods have shown that using a CNN-based backbone for extracting the spatial information and a decoder for extracting the global information is more effective than using a transformer-based backbone with a CNN-based decoder. This motivates us to adopt the CNN-based backbone using the MetaFormer block and design our MetaFormer-based decoder, which consists of a novel self-attention module to capture the global contexts. To consider both the global contexts extraction and the computational efficiency of the self-attention for semantic segmentation, we propose a Channel Reduction Attention (CRA) module that reduces the channel dimension of the query and key into the one dimension. In this way, our proposed MetaSeg outperforms the previous state-of-the-art methods with more efficient computational costs on popular semantic segmentation and a medical image segmentation benchmark, including ADE20K, Cityscapes, COCO-stuff, and Synapse.