Prominent expression method for line features in multiview reconstruction

Abstract

In multi-view reconstruction, mesh models are one of the most important data carriers. However, existing reconstruction methods have not adequately considered the existence of real-world edge features, resulting in insufficient utilization of geometric feature information from images. This leads to issues such as surface distortion and a lack of prominent edge features in the reconstructed mesh models. To address these challenges, this paper proposes a method of line features prominent expression during the multi-view reconstruction process. This algorithm integrates image line features and the mesh model to enhance geometric features. Specifically, in this paper, the line features on the image are first extracted, and the 2D line features are sampled and fitted to synthesize 3D line features by depth map. Then, it clusters the 3D line features and establishes minimum bounding boxes. Finally, the mesh line features are determined based on graph cut methods and are prominently expressed in the mesh model. Experimental validation demonstrates that in terms of the accuracy of line feature expression, the average reprojection pass rate for line features in five datasets reaches 93.7 %. This proves that this method significantly mitigates the issue of unclear edge features on the model surface, greatly improving the visual quality of mesh models while maintaining a high accuracy.