Abstract
Standard datasets often present limitations, particularly due to the fixed nature of input data sensors, which makes it difficult to compare methods that actively adjust sensor parameters to suit environmental conditions. This is the case with Automatic-Exposure (AE) methods, which rely on environmental factors to influence the image acquisition process. As a result, AE methods have traditionally been benchmarked in an online manner, rendering experiments non-reproducible. Building on our prior work, we propose a methodology that utilizes an emulator capable of generating images at any exposure time. This approach leverages BorealHDR, a unique multi-exposure stereo dataset, along with its new extension, in which data was acquired along a repeated trajectory at different times of the day to assess the impact of changing illumination. In total, BorealHDR covers 13.4 km over 59 trajectories in challenging lighting conditions. The dataset also includes lidar-inertial-odometry-based maps with pose estimation for each image frame, as well as Global Navigation Satellite System (GNSS) data for comparison. We demonstrate that by using images acquired at various exposure times, we can emulate realistic images with a Root-Mean-Square Error (RMSE) below 1.78% compared to ground truth images. Using this offline approach, we benchmarked eight AE methods, concluding that the classical AE method remains the field's best performer. To further support reproducibility, we provide in-depth details on the development of our backpack acquisition platform, including hardware, electrical components, and performance specifications. Additionally, we share valuable lessons learned from deploying the backpack over more than 25 km across various environments. Our code and dataset are available online at this link: this https URL BorealHDR
Abstract (translated)
标准数据集通常存在一些限制，特别是由于输入数据传感器的固有特性难以改变，这使得比较那些能够根据环境条件调整传感器参数的方法变得困难。这种情况尤其体现在自动曝光（AE）方法中，这些方法依赖于环境因素来影响图像获取过程。因此，传统的AE方法在线进行基准测试，导致实验不可重复。 基于我们之前的工作，我们提出了一种利用模拟器生成任意曝光时间图像的方法。这种方法使用了BorealHDR数据集，这是一个独特的多曝光立体视觉数据集，以及其新的扩展版本，在这个版本中，数据是在一天中的不同时间段沿着相同的轨迹收集的，以评估光照变化的影响。总体而言，BorealHDR涵盖了59条轨迹、13.4公里的距离，并在挑战性的照明条件下进行了采集。该数据集还包括基于激光雷达-惯性里程计的地图以及每帧图像的姿态估计，还提供了全球导航卫星系统（GNSS）数据进行比较。 我们展示了通过使用不同曝光时间获取的图像，可以生成与真实情况相比误差低于1.78%的逼真图像。利用这种方法，我们在离线状态下对八种AE方法进行了基准测试，并得出结论：经典的AE方法仍然是表现最佳的方法。为了进一步支持可重复性研究，我们提供了有关背包采集平台开发的详细信息，包括硬件、电气组件和性能规范。此外，我们也分享了在超过25公里的各种环境下部署背包所获得的经验教训。 我们的代码和数据集可通过以下链接获取：[BorealHDR](https://this https URL)
URL
https://arxiv.org/abs/2506.18844
