Achieving robust stereo 3D imaging under diverse illumination conditions is challenging due to the limited dynamic range of conventional cameras, causing existing stereo depth estimation methods to suffer from under- or over-exposed images. In this paper, we propose dual-exposure stereo that combines auto-exposure control and dual-exposure bracketing to achieve stereo 3D imaging with extended dynamic range. Specifically, we capture stereo image pairs with alternating dual exposures, which automatically adapt to scene illumination and effectively distribute the scene dynamic range across the dual-exposure frames. We then estimate stereo depth from these dual-exposure stereo images by compensating for motion between consecutive frames. To validate our approach, we develop a robotic vision system, acquire real-world HDR stereo video datasets, and generate additional synthetic datasets. Experimental results demonstrate that our method outperforms existing exposure control methods.

Integrating RGB and NIR stereo imaging provides complementary spectral information, potentially enhancing robotic 3D vision in challenging lighting conditions. However, existing datasets and imaging systems lack pixel-level alignment between RGB and NIR images, posing challenges for downstream vision tasks. In this paper, we introduce a robotic vision system equipped with pixel-aligned RGB-NIR stereo cameras and a LiDAR sensor mounted on a mobile robot. The system simultaneously captures pixel-aligned pairs of RGB stereo images, NIR stereo images, and temporally synchronized LiDAR points. Utilizing the mobility of the robot, we present a dataset containing continuous video frames under diverse lighting conditions. We then introduce two methods that utilize the pixel-aligned RGB-NIR images: an RGB-NIR image fusion method and a feature fusion method. The first approach enables existing RGB-pretrained vision models to directly utilize RGB-NIR information without fine-tuning. The second approach fine-tunes existing vision models to more effectively utilize RGB-NIR information. Experimental results demonstrate the effectiveness of using pixel-aligned RGB-NIR images across diverse lighting conditions.