FarSight: Long-Range Depth Estimation from Outdoor Images

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.