  Structure from Motion (SfM) using imagery that involves extreme appearance
changes is yet a challenging task due to a loss of feature repeatability. Using
feature correspondences obtained by matching densely extracted convolutional
neural network (CNN) features significantly improves the SfM reconstruction
capability. However, the reconstruction accuracy is limited by the spatial
resolution of the extracted CNN features which is not even pixel-level accuracy
in the existing approach. Providing dense feature matches with precise keypoint
positions is not trivial because of memory limitation and computational burden
of dense features. To achieve accurate SfM reconstruction with highly
repeatable dense features, we propose an SfM pipeline that uses dense CNN
features with relocalization of keypoint position that can efficiently and
accurately provide pixel-level feature correspondences. Then, we demonstrate on
the Aachen Day-Night dataset that the proposed SfM using dense CNN features
with the keypoint relocalization outperforms a state-of-the-art SfM (COLMAP
using RootSIFT) by a large margin.
