【泡泡一分钟】FarSight：从户外图像中实现远距离深度估计

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【泡泡一分钟】FarSight：从户外图像中实现远距离深度估计

2019 年 5 月 22 日 泡泡机器人SLAM

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标题：FarSight: Long-Range Depth Estimation from Outdoor Images

作者：Md. Alimoor Reza, Philip David and Jana Košecka

来源：2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

编译：明煜航

审核：颜青松，陈世浪

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摘要

本文研究了远距离的室外城市环境中进行单目深度估计的问题。深度传感器，或传统的深度估计算法（无论双目还是单目）预测的深度范围都限制在室外100米以及室内10米的范围内。也正是由于传感器的限制，使得数据集中缺少远距离的真值，在某种程度上导致了使用基于学习的室外单目方法先天存在缺陷。为了克服这个缺陷，作者首先提出了一种新颖的方法来生成远距离的模拟深度真值数据。作者利用了谷歌地球图像来重建不同城市的大范围且具有合适比例的三维模型。得到的模型库和对应的RGB图像以及远距离的深度渲染一起用作深度估计的训练数据。

随后作者训练了两个深度神经网络模型来估计远距离的深度值：i) 一个卷积神经网络 (CNN)和 ii) 一个生成对抗网络 (GAN)。作者从他们的实验中发现GAN模型预测的深度值更加精确。作者计划开源特闷的数据可以及极限模型。

Abstract

This paper introduces the problem of long-range monocular depth estimation for outdoor urban environments. Range sensors and traditional depth estimation algorithms (both stereo and single view) predict depth for distances of less than 100 meters in outdoor settings and 10 meters in indoor settings. The shortcomings of outdoor single view methods that use learning approaches are, to some extent, due to the lack of long-range ground truth training data, which in turn is due to limitations of range sensors. To circumvent this, we first propose a novel strategy for generating synthetic long-range ground truth depth data. We utilize Google Earth images to reconstruct large-scale 3D models of different cities with proper scale. The acquired repository of 3D models and associated RGB views along with their long-range depth renderings are used as training data for depth prediction. We then train two deep neural network models for long-range depth estimation: i) a Convolutional Neural Network (CNN) and ii) a Generative Adversarial Network (GAN). We found in our experiments that the GAN model predicts depth more accurately. We plan to open-source the database and the baseline models for public use.

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