通过双曝光提升动态范围，实现复杂光照下的高精度3D成像
Dual Exposure Stereo for Extended Dynamic Range 3D Imaging
- 采用自动双曝光控制，根据场景亮度差异动态调整曝光组合
- 在真实与合成数据集上，深度估计误差显著低于传统方法
- 适合机器人视觉、自动驾驶等强光暗对比场景
在多变光照条件下实现鲁棒的立体3D成像是一项重要但具挑战性的任务，因相机动态范围（DR）远小于真实世界。现有立体深度估计方法常受过曝或欠曝图像影响而精度下降。本文提出双曝光立体成像方法，设计自动双曝光控制策略，在场景动态范围超过相机时主动拉开两个曝光值，以获取更广范围的亮度信息。基于采集的双曝光立体图像，使用具备运动感知能力的双曝光立体网络进行深度估计。为验证方法有效性，构建了机器人视觉系统，采集立体视频数据集，并生成合成数据集。实验表明，该方法在深度估计性能上优于其他曝光控制策略。
原文摘要 · Abstract (English)
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.
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