Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI.

翻译：深度估计是自主系统评估自身状态与感知周围环境的基础知识。近年来，深度学习算法在深度估计中因能够克服主动深度感知系统的局限性而受到广泛关注。此外，由于单目摄像头成本低、体积小，研究者聚焦于单目深度估计（MDE），即从单张RGB视频帧中估计稠密深度图。当前最先进的MDE模型通常依赖高度深层且复杂的视觉Transformer（ViT）架构，这使得其在硬件受限设备上难以实现快速推理。为此，本文针对在嵌入式设备上利用ViT进行MDE的问题展开研究。此类系统通常具有有限的内存容量和低功耗CPU/GPU特性。我们提出METER——一种新型轻量级视觉Transformer架构，能够在嵌入式硬件（NVIDIA Jetson TX1和NVIDIA Jetson Nano）上实现最先进的估计性能与低延迟推理。我们提供了包含三种替代配置的METER方案，一种用于平衡像素估计与图像细节重建的新型损失函数，以及一种改进整体最终预测的新数据增强策略。所提方法在两个基准数据集（室内NYU Depth v2和室外KITTI）上均优于现有轻量级工作。