Achieving robust stereo 3D imaging under diverse illumination conditions is an important however challenging task, due to the limited dynamic ranges (DRs) of cameras, which are significantly smaller than real world DR. As a result, the accuracy of existing stereo depth estimation methods is often compromised by under- or over-exposed images. Here, we introduce dual-exposure stereo for extended dynamic range 3D imaging. We develop automatic dual-exposure control method that adjusts the dual exposures, diverging them when the scene DR exceeds the camera DR, thereby providing information about broader DR. From the captured dual-exposure stereo images, we estimate depth using motion-aware dual-exposure stereo network. To validate our method, we develop a robot-vision system, collect stereo video datasets, and generate a synthetic dataset. Our method outperforms other exposure control methods.

翻译：在不同光照条件下实现鲁棒的立体三维成像是一项重要但具有挑战性的任务，这源于相机有限的动态范围远小于真实世界的动态范围。因此，现有立体深度估计方法的精度常因曝光不足或过曝的图像而受到影响。本文提出双曝光立体视觉用于扩展动态范围三维成像。我们开发了一种自动双曝光控制方法，该方法根据场景动态范围调整双曝光参数——当场景动态范围超过相机动态范围时，使双曝光参数产生差异，从而提供更宽动态范围的信息。基于捕获的双曝光立体图像，我们利用运动感知双曝光立体网络进行深度估计。为验证本方法，我们开发了机器人视觉系统，采集了立体视频数据集，并生成了合成数据集。实验表明，本方法优于其他曝光控制方法。