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.

翻译：我们提出了一种基于街景RGB图像的多类目标地理定位与高度估计联合处理流程，该流程将街景图像视为唯一可用的输入数据模态。本方案通过马尔可夫随机场优化实现确定性输出。该技术利用图像元数据以及自定义训练的卷积神经网络在图像平面中检测到的目标坐标。在目标地理定位基础上，采用本方法计算目标高度对整体计算成本的影响可忽略不计。实验结果表明，该方法对排水管和交通标志的平均高程估计误差低于20厘米。