## 3 High-Quality ToF Camera Data Acquisition Strategies
### Pixel-Level Depth Data Labeling Techniques Using 3D Simulation Data  
This study, unlike segmentation and edge smoothing techniques based on RGB-D [15], utilizes time-of-flight (ToF) cameras to enhance pixel-level accuracy by obtaining precise distance values corresponding to the pixel values captured by the camera. To achieve this, the creation of highly precise measuring equipment is essential, and obtaining real distance values from depth data captured in various environments is almost impossible. Even when additional equipment such as Lidar is installed and synchronized with the ToF sensor, discrepancies due to installation location can cause significant errors, making it difficult to label depth information at pixel-level distances [15; 16]. In this research, a new method is proposed that compares point cloud data captured with a ToF camera with data obtained through 3D simulation to compensate for inaccuracies. This method leverages the simulation environment set up to check the accuracy and interference levels of part-by-part 3D drawings and production equipment easily obtainable during the design and manufacturing processes in industrial settings such as automotive factories. Precision 3D models are created to millimeter accuracy, and actual vehicle components are also precisely manufactured based on these 3D models, with tolerance management in place. The application environment that captures depth information using real ToF sensors is simulated, and the actual environment's location and viewpoint captured by the ToF sensor are replicated to obtain simulation data. This approach ensures the accuracy of the depth data, even for automotive products with intricate curves and contours, making it sufficient for use as labeling values for training ToF sensor data. Furthermore, the accurate depth information obtained in the simulation environment can effectively enhance the accuracy of AI algorithms aimed at improving ToF sensor data. The Unity simulation environment was used for this study. In Unity, modeling of ToF sensors is possible, and techniques for capturing the viewpoint used by ToF sensors in real environments can be easily employed, facilitating the acquisition of depth data in virtual environments. As illustrated in Figure 4, the ToF shooting location and viewpoint in the real environment are set identically in the Unity 3D simulation environment, allowing the depth values captured by the ToF sensor in the actual environment to be obtained. When capturing depth values in the Unity virtual environment, it is imperative to capture  
Figure 3: Changes by ToF data preprocessing process. at the highest possible resolution to ensure that the depth values of component surfaces match the actual component depths, addressing issues caused by data resolution that could prevent the capture of curved surface data. Moreover, accurately synchronizing the ToF capture location and viewpoint from the real environment to the virtual environment is challenging, and this study identified the need for additional technological development to effectively synchronize the real and simulation environments in real-time using SLAM information from mobile robots in the actual environment. The ToF depth data captured in the simulator and the noise-removed depth data from actual captures in Section 3.1 do not precisely match due to discrepancies in location and viewpoint between the real and virtual environments. To use the virtual environment data as labeling values for real environment data, it is necessary to match each pixel precisely. To address this issue, a shape-matching process using the ICP algorithm was undertaken, as shown in Figure 5. Even after alignment, the higher-resolution virtual environment depth data was downsampled to match the real environment depth data on a 1:1 basis.