A Robust Error-Resistant View Selection Method for 3D Reconstruction
This work addresses a specific bottleneck in 3D reconstruction for computer vision applications, offering incremental improvements in accuracy.
The paper tackles the problem of increased triangulation uncertainty in Structure from Motion view selection by proposing a robust error-resistant method, which reduces average reprojection error by 29.40% and absolute trajectory error by 5.07% on the TUM and DTU datasets compared to an exhaustive baseline.
To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-resistant matrix. The sorting results of each row in the error-resistant matrix determine the candidate view set for each view. By traversing the candidate view sets of all views and completing the missing views based on the error-resistant matrix, the integrity of 3D reconstruction is ensured. Experimental comparisons between this method and the exhaustive method with the highest accuracy in the COLMAP program are conducted in terms of average reprojection error and absolute trajectory error in the reconstruction results. The proposed method demonstrates an average reduction of 29.40% in reprojection error accuracy and 5.07% in absolute trajectory error on the TUM dataset and DTU dataset.