Combining geolocation and height estimation of objects from street level imagery
This work addresses a domain-specific need for precise object mapping in urban environments, but it is incremental as it builds on existing methods like CNNs and Markov Random Fields.
The paper tackles the problem of estimating both the geolocation and height of objects from street-level RGB imagery, achieving an average elevation estimation error of less than 20 cm for water drains and road signs.
We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with deterministic output. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network. Computing the object height using our methodology, in addition to object geolocation, has negligible effect on the overall computational cost. Accuracy is demonstrated experimentally for water drains and road signs on which we achieve average elevation estimation error lower than 20cm.