Pipeline Inner Surface 3D Reconstruction and Depth Prediction Based on Fast-MVSNet for Intelligent Sewer Robot Vision

Ensuring the regular operation of drainage pipes has significant implications for the maintenance of regular city order. Currently, the inspections of urban drainage pipes are mostly based on monocular CCTV videos. However, these videos only provide simple 2D images without localization information, which makes it challenging to comprehend the complete internal details of the pipelines. Therefore, this paper investigates 3D reconstruction and depth prediction based on monocular vision, in order to restore the pipeline information in real scenes through the monocular video acquired by the robotic CCTV inspection system, and to provide assistance in locating the disease at a later stage. ORB-SLAM3 is employed to estimate the camera's extrinsic parameters for each of the views to obtain the camera poses and uses the currently more innovative Fast-MVSN et for model training, and this paper uses the model to predict depth maps of the pipeline image. Finally, outliers are filtered through photometric consistency and geometric continuity to suppress noise during the reconstruction process and ensure a certain degree of completeness and accuracy. On the pipeline test dataset, the best model in this framework achieves an absolute relative error of 0.458 in depth prediction, which enables obtaining a denser 3D point cloud scene of the pipeline.