Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes. Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore structural features and spatial image information, Single Image Depth Estimation (SIDE) is often highlighted in scopes of scientific and technological innovation, as this concept provides advantages related to its low implementation cost and robustness to environmental conditions. In the context of autonomous vehicles, state-of-the-art CNNs optimize the SIDE task by producing high-quality depth maps, which are essential during the autonomous navigation process in different locations. However, such networks are usually supervised by sparse and noisy depth data, from Light Detection and Ranging (LiDAR) laser scans, and are carried out at high computational cost, requiring high-performance Graphic Processing Units (GPUs). Therefore, we propose a new lightweight and fast supervised CNN architecture combined with novel feature extraction models which are designed for real-world autonomous navigation. We also introduce an efficient surface normals module, jointly with a simple geometric 2.5D loss function, to solve SIDE problems. We also innovate by incorporating multiple Deep Learning techniques, such as the use of densification algorithms and additional semantic, surface normals and depth information to train our framework. The method introduced in this work focuses on robotic applications in indoor and outdoor environments and its results are evaluated on the competitive and publicly available NYU Depth V2 and KITTI Depth datasets.

中文翻译：

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深度学习技术提升自主导航单目深度估计

推断图像的深度是计算机视觉领域中的一个基本逆问题，因为深度信息是通过 2D 图像获得的，可以从观察到的真实场景的无限可能性中生成。受益于卷积神经网络 (CNN) 在探索结构特征和空间图像信息方面的进步，单图像深度估计 (SIDE) 在科技创新范围内经常受到重视，因为该概念具有与实现成本低和对环境条件的稳健性。在自动驾驶汽车的背景下，最先进的 CNN 通过生成高质量的深度图来优化 SIDE 任务，这在不同位置的自主导航过程中必不可少。然而，这种网络通常由来自光检测和测距 (LiDAR) 激光扫描的稀疏和嘈杂的深度数据监督，并且以高计算成本执行，需要高性能图形处理单元 (GPU)。因此，我们提出了一种新的轻量级、快速监督的 CNN 架构，并结合了专为现实世界自主导航设计的新颖特征提取模型。我们还引入了一个高效的表面法线模块，结合一个简单的几何 2.5D 损失函数来解决 SIDE 问题。我们还通过结合多种深度学习技术进行创新，例如使用致密化算法和额外的语义、表面法线和深度信息来训练我们的框架。