Dual Exposure Stereo for Extended Dynamic Range 3D Imaging
This addresses the challenge of robust 3D imaging for applications like robotics, though it appears incremental as it builds on existing stereo methods with a novel exposure control approach.
The paper tackles the problem of stereo 3D imaging under diverse illumination by introducing dual-exposure stereo to extend dynamic range, resulting in improved depth estimation accuracy that outperforms other exposure control methods.
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.