## 2 Related Work  
Time-of-flight (ToF) cameras provide high-resolution depth information at a low cost, making them widely used in various applications and robotic technologies. ToF cameras operate by measuring the time it takes for light to travel to an object and back, calculating the distance based on this time-of-flight principle. However, these cameras are sensitive to environmental factors such as temperature changes, dust, and distance variability, which can decrease the accuracy of the depth information. Various studies are underway to address these issues. [PERSON]'s research evaluated the impact of environmental factors on the sensors and laser depth measurements of ToF cameras and aimed to eliminate key environmental factors affecting ToF camera accuracy based on this evaluation data. However, the tests were conducted only in temperature-controlled laboratory environments, limiting the reflection of complex interactions of other variables [5]. [PERSON]'s research developed the Cromo algorithm to enhance depth accuracy by minimizing optical distortion and sensor noise, maintaining ToF camera performance under various lighting conditions. However, this method proved effective under highly controlled laboratory conditions but insufficient in reflecting the variability of real environments [6]. [PERSON]'s research used specially coated lenses and software algorithms to reduce interactions between laser beams and dust for ToF cameras. This approach improved ToF performance under dusty conditions but did not address other environmental factors, making it inadequate for use in actual industrial settings [7]. Given the limitations in improving depth accuracy through practical ToF performance enhancements, recent research has focused on using RGB-D technology to simultaneously collect RGB image information and depth information, combining color and spatial information to enhance depth accuracy. This technology aims to more accurately recognize, segment, and classify captured objects rather than directly improving ToF depth accuracy. For instance, RGB-D technology allows for clearer identification of objects within an image, enabling more precise reconstruction of object depth [8; 9]. Furthermore, research is being conducted on algorithms that reconstruct identified objects into 3D models using RGB-D sensors [10; 11]. This technology can create accurate 3D object models even with imprecise RGB-D data and is particularly useful for reconstructing complex-shaped objects in 3D. This expands the applicability in fields such as digital content creation, virtual reality, and augmented reality. However, these deep learning-based RGB-D technologies primarily focus on utilizing approximate depth information to identify objects in images. This approach may not be suitable for industrial environments that require precise depth measurements. Considering the characteristics of ToF cameras, their use is recommended only in environments where a certain margin of error in measured depth is acceptable. If the sensing accuracy of ToF cameras can be improved, it is expected that ToF sensors can be utilized in industrial settings that require precise depth data measurement. The difficulty in improving the depth accuracy of ToF cameras lies in knowing the exact distance values of depth data measured by actual ToF cameras. While it is possible to measure depth accuracy using high-precision equipment in specific environments during the ToF camera manufacturing stage, improving the accuracy of ToF cameras, which are influenced by various environmental factors and measurement target components, requires knowing the exact distance values of depth data measured in actual usage environments. Additionally, to develop AI algorithms, which have been actively researched recently, accurate depth values of depth data measured by ToF cameras must be used as labeling values. However, obtaining such precise depth values is virtually impossible, which has hindered research on improving the pixel-level accuracy of ToF cameras.