3D Reconstruction and Accuracy Evaluation of Ancient Chinese Architectural Patches Based on Depth Learning from Single Image

This paper primarily explores the depth learning method from a single image under the current unsupervised framework. It investigates whether this method can effectively deal with the repetition of the inherent structure and texture of ancient Chinese architectural images and whether it can meet the centimeter-level reconstruction accuracy required by the Chinese architecture documentation standard. Specifically, the accuracy difference of depth learning based on a single image under the image acquisition mode of fixed binocular cameras and the image acquisition mode of a single moving camera is compared using the data obtained by the structured light depth camera as the ground truth by directly comparing the depth map and the three-dimensional (3D) point cloud. The experimental results show that while 3D reconstruction based on multiple images is challenging due to the existence of repeated structures and textures, the impact of the existence on depth learning based on a single image is generally insignificant. In addition, even though depth learning based on a single image has achieved comparable accuracy with laser scanning on many open indoor and outdoor datasets, it is still difficult to achieve the centimeter-level reconstruction accuracy required by the digital documentation standard of ancient Chinese architectural 3D reconstruction. In the future, the shape of prior information will be exploited to improve the reconstruction accuracy.