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Real-world autonomous driving datasets comprise of images aggregated fromdifferent drives on the road. The ability to relight captured scenes to unseenlighting conditions, in a controllable manner, presents an opportunity toaugment datasets with a richer variety of lighting conditions, similar to whatwould be encountered in the real-world. This paper presents a novel image-basedrelighting pipeline, SIMBAR, that can work with a single image as input. To thebest of our knowledge, there is no prior work on scene relighting leveragingexplicit geometric representations from a single image. We present qualitativecomparisons with prior multi-view scene relighting baselines. To furthervalidate and effectively quantify the benefit of leveraging SIMBAR for dataaugmentation for automated driving vision tasks, object detection and trackingexperiments are conducted with a state-of-the-art method, a Multiple ObjectTracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack onSIMBAR-augmented KITTI - an impressive 9.0% relative improvement over thebaseline MOTA of 85.6% with CenterTrack on original KITTI, both models trainedfrom scratch and tested on Virtual KITTI. For more details and SIMBAR relitdatasets, please visit our project website (https://simbarv1.github.io/).

SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks