2019/10/15 by Eunah Jung, Nan Yang, Jung, Eunah +3 · 2 citations
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Image Processing Techniques and Applications #Optical measurement and interference techniques #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.1910.06632
openalex publication_date 2019/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.