Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable manner, presents an opportunity to augment datasets with a richer variety of lighting conditions, similar to what would be encountered in the real-world. This paper presents a novel image-based relighting pipeline, SIMBAR, that can work with a single image as input. To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image. We present qualitative comparisons with prior multi-view scene relighting baselines. To further validate and effectively quantify the benefit of leveraging SIMBAR for data augmentation for automated driving vision tasks, object detection and tracking experiments are conducted with a state-of-the-art method, a Multiple Object Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained from scratch and tested on Virtual KITTI. For more details and SIMBAR relit datasets, please visit our project website (https://simbarv1.github.io/).

本文介绍了一种新的基于图像的重新照明管道SIMBAR，可以使用单个图像作为输入来改善现有的自动驾驶视觉任务数据集，通过与基准方法进行实证比较和对象检测跟踪实验，结果显示SIMBAR的数据增强效果显著，取得了很好的实验成果。

SIMBAR: 用单一图像实现场景光照重构，用于自动驾驶视觉任务的有效数据增强