Learning Feature Subspaces for Appearance-Based Bundle Adjustment

Chia-Ming Cheng, Hwann-Tzong Chen

Abstract: We present an improved bundle adjustment method based on the online learned appearance subspaces of 3D points. Our method incorporates the additional information from the learned appearance models into bundle adjustment. Through the online learning of the appearance models, we are able to include more plausible observations of 2D features across diverse viewpoints. Bundle adjustment can benefit from such an increase in the number of observations. Our formulation uses the appearance information to impose additional constraints on the optimization. The detailed experiments with ground-truth data show that the proposed method is able to enhance the reliability of 2D correspondences, and more important, can improve the accuracy of camera motion estimation and the overall quality of 3D reconstruction.