Research on 3D reconstruction algorithms for joint building edge contours
In response to the insufficiency in utilizing line features and the low reconstruction accuracy in existing 3D reconstruction research,this study proposes a 3D reconstruction algorithm joint for building footprint contours. Firstly,the Bi-Directional Cascade Network (BDCN ) model and corresponding preprocessing algorithms are employed to extract edge contours from 2D images. Next,initial matching hypotheses for line segments are established based on the epipolar geometry principles and triangulation principles. Subsequently,a combination of similarity calculations and graph clustering algorithms is used to reconstruct the 3D contour line model. Finally,the results are applied to the optimization of the 3D mesh model. Experimental validation of this algorithm is conducted using the Building-Imagery-P36 and DTU MVS datasets. The results demonstrate that the method presented in this study produces concise and complete contour line segments. Compared to the classical Line3D++algorithm,it exhibits significant improvements in both reconstruction accuracy and efficiency. Furthermore,with the additional 3D contour line constraints,the generated mesh models offer enhanced visual quality and richer detail information.
3D reconstructionline featuregrid optimizationgraph theorydeep learningcontour extraction
万方数据
维普
