  We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its
potential as an image sequence enhancement for stereo visual odometry in low
light conditions. We base our method on an invertible adversarial network to
transfer the beneficial features of brightly illuminated scenes to the sequence
in poor illumination without costly paired datasets. In order to preserve the
coherent geometric cues for the translated sequence, we present a novel network
architecture as well as a novel loss term combining temporal and stereo
consistencies based on optical flow estimation. We demonstrate that the
enhanced sequences improve the performance of state-of-the-art feature-based
and direct stereo visual odometry methods on both synthetic and real datasets
in challenging illumination. We also show that MFGAN outperforms other
state-of-the-art image enhancement and style transfer methods by a large margin
in terms of visual odometry.
