We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo depth prediction branch which are combined in a final fusion stage. Each block is supervised through a combination of supervised and gated self-supervision losses. To facilitate training and validation, we acquire a long-range synchronized gated stereo dataset for automotive scenarios. We find that the method achieves an improvement of more than 50 % MAE compared to the next best RGB stereo method, and 74 % MAE to existing monocular gated methods for distances up to 160 m. Our code,models and datasets are available here.

翻译：我们提出门控立体视觉（Gated Stereo），一种基于主动门控立体图像的高分辨率、长距离深度估计技术。该方法利用主动高动态范围被动捕捉图像，融合多视角线索与来自主动门控的飞行时间强度线索。为此，我们设计了一种深度估计方法，包含单目与立体深度预测分支，并在最终融合阶段进行整合。每个分支通过监督损失与门控自监督损失组合进行训练。为便于训练与验证，我们采集了一套面向自动驾驶场景的长距离同步门控立体数据集。实验表明，该方法在距离达160米时，相较于最优的RGB立体方法，平均绝对误差（MAE）降低超过50%；相较于现有单目门控方法，MAE降低74%。我们的代码、模型及数据集均已在公开平台发布。