论文标题
基于梯度的单眼深度估计的不确定性
Gradient-based Uncertainty for Monocular Depth Estimation
论文作者
论文摘要
在单眼深度估计中，图像上下文中的干扰（例如移动对象或反射材料）很容易导致错误的预测。因此，每个像素的不确定性估计是必要的，尤其是针对自动驾驶等安全至关重要的应用。我们提出了以深神经网络为代表的已经训练的已训练的深度估计模型的事后不确定性估计方法。不确定性是用辅助损失函数提取的梯度估计的。为了避免依靠地面真实信息作为损失定义，我们根据图像的深度预测及其水平翻转的对应关系提出了辅助损失函数。我们的方法可实现Kitti和Nyu深度V2基准的最新不确定性估计结果，而无需重新训练神经网络。模型和代码可在https://github.com/jhornauer/grumodepth上公开获得。
In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous predictions. For that reason, uncertainty estimates for each pixel are necessary, in particular for safety-critical applications such as automated driving. We propose a post hoc uncertainty estimation approach for an already trained and thus fixed depth estimation model, represented by a deep neural network. The uncertainty is estimated with the gradients which are extracted with an auxiliary loss function. To avoid relying on ground-truth information for the loss definition, we present an auxiliary loss function based on the correspondence of the depth prediction for an image and its horizontally flipped counterpart. Our approach achieves state-of-the-art uncertainty estimation results on the KITTI and NYU Depth V2 benchmarks without the need to retrain the neural network. Models and code are publicly available at https://github.com/jhornauer/GrUMoDepth.
