Abstract
Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas. At the same time, we obtain a coarse matching set of feature points using pairwise polar geometric constraints. The positional results from the geometric constraints of the calibration plate are then used to filter out high-precision matching points, thereby improving the accuracy of the three-dimensional reconstruction. Experimental results demonstrate that the proposed method achieves superior reconstruction completeness and accuracy. In three real-world experiments, the average re-projection errors were 0.5245, 0.4151, and 0.4996 pixels, outperforming competing methods. This approach ensures robust pose estimation and facilitates precise 3D reconstructions.