Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.

► We proposes robust refinement methods to improve the 3D reconstruction algorithm by Furukawa et al. ► We improve the robustness by removing outliers based on a filtering approach. ► We divide 3D points into buckets for the bundle adjustment, removing outliers and merging results. ► The residuals are used to filter potential outliers to reduce the re-projection error. ►The mean re-projection error is reduced from about 47.6 to 2.13.