Augmenting Depth Estimation with Geospatial Context

TL;DR

This paper introduces a novel method for depth estimation that leverages geospatial context, specifically using overhead imagery to improve accuracy in ground-level depth predictions.

Contribution

It presents an end-to-end architecture that integrates overhead geospatial data into depth estimation, extending datasets for comprehensive evaluation.

Findings

Significant reduction in depth estimation error using geospatial context.

Effective at both close-range and long-distance scenarios.

Enhanced dataset with overhead imagery and height maps for evaluation.

Abstract

Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture location is known, the corresponding overhead viewpoint offers a valuable resource for understanding the scale of the scene. We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network. To support evaluation of our methods, we extend a recently released dataset with overhead imagery and corresponding height maps. Results demonstrate that integrating geospatial context significantly reduces error compared to baselines, both at close ranges and when evaluating at much larger distances than existing benchmarks consider.