Monocular Depth Estimation Using Synthetic Images With Shadow Removal
Rui Guo, Babajide Odunitan Ayinde, Hao Sun, Haritha Muralidharan, Kentaro Oguchi · 2019
Learning based monocular depth estimation has become popular in recent years. However, training of reliable depth estimators requires large volumes of ground truth depth data, which is expensive to obtain. To overcome this challenge, we propose a novel monocular depth estimation system that adopts pixel-perfect synthetic image training. The model is trained with synthetic data but inferred with realistic images by applying image domain adaptation. Considering realistic constraints, such as shadow regions, which cause the performance drops in depth estimation, the system adopts a dedicated module to remove such ambient shadows from images to guarantee premier performance in the task. Experimental results, with both synthetic and realistic benchmarks, indicate the efficacy and the advantages of the system compared to existing state-of-the-art technologies.