Self-supervised deep learning methods for depth and egomotion estimation can yield accurate trajectories without the need for ground-truth training data. However, as these approaches are based on photometric losses, their performance degrades significantly when used on sequences captured at night. This is because the fundamental assumptions of illumination consistency are not satisfied by point illumination sources such as car headlights and street lights. We propose a simple, yet effective, per-pixel neural intensity transformation to compensate for these illumination artefacts that occur between subsequent frames. In addition, we extend the rigid-motion transformation generated by the estimated depth and ego-motion to address issues of dynamic objects, noise, and blur using a sparse-reconstruction residual module. Lastly, a novel delta-loss is proposed to mitigate occlusions and reflections by making the estimated photometric losses bidirectionally consistent. Extensive experimental and ablation studies are performed on the challenging Oxford RobotCar dataset to demonstrate the efficacy of the proposed method, both in day-time and night-time sequences. The resulting framework is shown to outperform the current state of the art by 15\% in the RMSE metric over day and night time sequences.
