User-Guided Correction of Reconstruction Errors in Structure-from-Motion

We propose a user-guided method to correct reconstruction errors in Structure-from-Motion (SfM) processes. SfM takes a set of camera images as input and then estimates the cameras’ poses and three-dimensional point clouds based on keypoint matching. However, scenes with repetitive or similar structures often result in false matches, leading to inaccuracies in camera pose estimation. While automatic methods for removing false matches exist, achieving perfect accuracy with them remains challenging. Conversely, human intervention can ensure high accuracy, but manual identification and elimination of false matches is a tedious and error-prone process. Our proposed method strikes a balance by introducing a more efficient user-guided approach. Users provide approximate camera poses, which the system then uses to detect false matches. Specifically, the system examines overlaps between view frustums of camera pairs post user adjustments, classifying pairs as false matches if no overlap is found. This method leverages the user’s recollection of camera movements during scene capture to guide the reconstruction process. Evaluation with test cases and a user study confirm that our technique can efficiently remove false matches and enable accurate reconstruction of camera poses.