Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts

A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images

The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.

Denoising for 3D Point Cloud Based on Regularization of a Statistical Low-Dimensional Manifold

A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noise, a new regularization algorithm for denoising was proposed. In view of the fact that 3D point clouds have low-dimensional structures, a statistical low-dimensional manifold (SLDM) model was established. By regularizing its dimensions, the denoising problem of the point cloud was expressed as an optimization problem based on the geometric constraints of the regularization term of the manifold. A low-dimensional smooth manifold model was constructed by discrete sampling, and solved by means of a statistical method and an alternating iterative method. The performance of the denoising algorithm was quantitatively evaluated from three aspects, i.e., the signal-to-noise ratio (SNR), mean square error (MSE) and structural similarity (SSIM). Analysis and comparison of performance showed that compared with the algebraic point-set surface (APSS), non-local denoising (NLD) and feature graph learning (FGL) algorithms, the mean SNR of the point cloud denoised using the proposed method increased by 1.22 DB, 1.81 DB and 1.20 DB, respectively, its mean MSE decreased by 0.096, 0.086 and 0.076, respectively, and its mean SSIM decreased by 0.023, 0.022 and 0.020, respectively, which shows that the proposed method is more effective in eliminating Gaussian noise and Laplace noise in common point clouds. The application cases showed that the proposed algorithm can retain the geometric feature information of point clouds while eliminating complex noise.