Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications. Promptly obtaining accurate and efficient depth information allows for a rapid response in dynamic environments. Sensor-based methods using LIDAR and RADAR obtain high precision at the cost of high power consumption, price, and volume. While due to advances in deep learning, vision-based approaches have recently received much attention and can overcome these drawbacks. In this work, we explore an extreme scenario in vision-based settings: estimate a depth map from one monocular image severely plagued by grid artifacts and blurry edges. To address this scenario, We first design a convolutional attention mechanism block (CAMB) which consists of channel attention and spatial attention sequentially and insert these CAMBs into skip connections. As a result, our novel approach can find the focus of current image with minimal overhead and avoid losses of depth features. Next, by combining the depth value, the gradients of X axis, Y axis and diagonal directions, and the structural similarity index measure (SSIM), we propose our novel loss function. Moreover, we utilize pixel blocks to accelerate the computation of the loss function. Finally, we show, through comprehensive experiments on two large-scale image datasets, i.e. KITTI and NYU-V2, that our method outperforms several representative baselines.
翻译:深度信息是感知的基础,是自主驱动、机器人和其他受源限制的应用所必不可少的。 迅速获得准确而高效的深度信息,可以在动态环境中迅速作出反应。 使用LIDAR和RADAR的传感器方法以高电耗、价格和体积成本获得高度精准性。 由于深层学习的进步,基于愿景的方法最近受到极大关注,并可以克服这些缺陷。 在这项工作中,我们探索了基于愿景的环境下的极端情景:从一个被电网文物和模糊边缘严重困扰的单视图像中估算出一个深度地图。 为了应对这一情景,我们首先设计一个由频道关注和空间关注组成的动态关注机制块(CAMB),然后将这些CAMB插入到高电流连接中。 因此,我们的新方法可以找到当前图像的焦点,其管理管理程度极小,避免深度特征损失。 其次,我们通过深度价值、 X轴、 Y轴和对角方向梯度方向的梯度以及结构相似的指数测量(SSIM),我们提出了我们的新的损失损失功能。 此外,我们利用双级的图像模型模型模型, 加速计算。