Improving completeness and accuracy of 3D point clouds by using deep learning for applications of digital twins to civil structures

In the Architecture, Engineering, and Construction (AEC) sector, digital twins rely on precise 3D models to convey digital information about physical structures in a virtual space. However, due to the vulnerability to measurement errors in weak-textured regions, 3D point clouds generated by conventional photometric consistency or deep learning-based Multi-View Stereo (MVS) algorithms are often incomplete or inaccurate. Therefore, this paper integrates the consistency constraint across multiple views and the inferential capacity of deep learning to propose a novel approach for refining the missing regions of the depth maps generated by photoconsistency based MVS algorithms. The proposed solution involves a cost volume pyramid-based depth completion (CVP-DC) network with three multi-level pyramid structures, which sequentially estimates and completes depth maps in a coarse-to-fine manner. A dataset that consists of input images and the corresponding depth maps generated by photo-consistency based MVS algorithms, along with output ground truth depth maps, is developed using an open DTU MVS dataset. CVP-DC demonstrates competitive performance when tested on the public DTU MVS dataset, outperforming existing MVS algorithms in terms of both completeness and accuracy. Additionally, experimental studies are conducted utilizing UAV-collected RTK (Real-Time Kinematic) images of an outdoor bridge pier to reconstruct point clouds with absolute scales. Experimental validations demonstrate the effectiveness and applicability of the proposed approach in filling uneven and incomplete depth maps, thereby enhancing the completeness of the generated point clouds. The proposed approach holds promise for establishing precise 3D models for the digital twin of the AEC sector.