3D-scanning system based on monocular vision

Abstract

Monocular vision-based 3D-scanning systems have revolutionized object and environment capture. The authors have developed a system that uses a rotating camera and an angle sensor to capture images from different angles. Through deep learning techniques, depth maps are extracted from the images. The resulting depth map is used to generate a 3D point cloud model, which is preprocessed to enhance efficiency. This research considered several methods to improve depth accuracy of panorama image, as well as to recalibrate depth maps and smooth 360-degree 3D models. Our method produces better results than the pre-trained model. The authors use a LIDAR to get distance and increase accuracy of model. To visualize the model, the web framework was developed. The rendered model can be accessed through a web browser, providing functionalities such as coordinate selection and distance calculation.