论文标题
3D重建的封闭图像的不确定性深度估计
Uncertainty depth estimation with gated images for 3D reconstruction
论文作者
论文摘要
封闭的成像是一种用于自动驾驶汽车的新兴传感器技术，即使在不利的天气影响下，也可以提供高对比度图像。已经表明，该技术甚至可以生成具有与扫描LIDAR系统相当的准确性的高保真密度深度图。在这项工作中，我们扩展了最近的Gated2 Depth框架，并具有不确定性，从而为深度估计提供了额外的置信度度量。这种信心可以帮助滤除没有任何照明的区域中不确定的估计。此外，我们表明，对激光雷达深度完成算法产生的密集深度图培训可以进一步提高性能。
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence. It has been shown that this technology can even generate high-fidelity dense depth maps with accuracy comparable to scanning LiDAR systems. In this work, we extend the recent Gated2Depth framework with aleatoric uncertainty providing an additional confidence measure for the depth estimates. This confidence can help to filter out uncertain estimations in regions without any illumination. Moreover, we show that training on dense depth maps generated by LiDAR depth completion algorithms can further improve the performance.
