3D Scan

3D scanning aims to create accurate digital representations of real-world objects and environments, primarily focusing on overcoming limitations of raw scan data like noise and incompleteness. Current research emphasizes improving accuracy and efficiency through techniques like augmented reality interfaces for manual refinement, leveraging deep learning models (e.g., GANs, U-Nets, variational autoencoders) for tasks such as anomaly detection, object segmentation, and noise reduction, and developing novel algorithms for point cloud registration and shape completion. These advancements are significantly impacting various fields, including cultural heritage preservation, manufacturing quality control, robotics, and virtual/augmented reality applications, by enabling more robust and efficient 3D data processing.