Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality and so on. This topic represents a very challenging task and the advent of deep learning enabled to tackle this problem with excellent results. However, these architectures are extremely deep and complex. Thus, real-time performance can be achieved only leveraging on power-hungry GPUs that do not allow to infer depth maps in application fields characterized by low-power constraints. To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image. Similarly to stateof-the-art, we train our network in an unsupervised manner casting depth estimation as an image reconstruction problem. Extensive experimental results on the KITTI 2015 dataset show that compared to the top performing approach our network has similar accuracy but a much lower complexity (about 6% of parameters) enabling to infer a depth map for a KITTI image in about 1.7 s on the Raspberry Pi 3 and at more than 8 Hz on a standard CPU. Moreover, by trading accuracy for efficiency, our network allows to infer maps at more than 2 Hz and 40 Hz respectively, still being more accurate than most state-of-the-art slower methods. To the best of our knowledge, it is the first method enabling such performance on CPUs paving the way for effective deployment of unsupervised monocular depth estimation even on embedded systems.

本文提出了一种新的CPU架构，使用从单个输入图像提取的特征金字塔快速推断准确的深度图，与现有的方法相比，模型的参数较少（约为6％），使得其能在Raspberry Pi 3上推断KITTI图像的深度图约1.7秒，为CPU上的嵌入式系统的无监督单目深度估计的快速有效部署铺平了道路。

实现CPU上实时无监督单目深度估计