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.

翻译：针对运动恢复结构（SFM）视图选择中因选取相机基线较小的视图导致三角测量不确定性增加的问题，本文提出一种鲁棒抗误差的视图选择方法。该方法通过基于三角测量的计算获取抗误差模型，进而构建抗误差矩阵。利用抗误差矩阵各行排序结果确定每个视图的候选视图集，通过遍历所有视图的候选视图集并基于抗误差矩阵完成缺失视图的补全，从而保证三维重建的完整性。将本方法与COLMAP程序中精度最高的穷举法进行实验对比，分别比较重建结果的平均重投影误差和绝对轨迹误差。在TUM数据集与DTU数据集上，本方法在重投影误差精度上平均降低29.40%，在绝对轨迹误差上平均降低5.07%。