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The success of deep learning in computer vision is based on availability oflarge annotated datasets. To lower the need for hand labeled images, virtuallyrendered 3D worlds have recently gained popularity. Creating realistic 3Dcontent is challenging on its own and requires significant human effort. Inthis work, we propose an alternative paradigm which combines real and syntheticdata for learning semantic instance segmentation and object detection models.Exploiting the fact that not all aspects of the scene are equally important forthis task, we propose to augment real-world imagery with virtual objects of thetarget category. Capturing real-world images at large scale is easy and cheap,and directly provides real background appearances without the need for creatingcomplex 3D models of the environment. We present an efficient procedure toaugment real images with virtual objects. This allows us to create realisticcomposite images which exhibit both realistic background appearance and a largenumber of complex object arrangements. In contrast to modeling complete 3Denvironments, our augmentation approach requires only a few user interactionsin combination with 3D shapes of the target object. Through extensiveexperimentation, we conclude the right set of parameters to produce augmenteddata which can maximally enhance the performance of instance segmentationmodels. Further, we demonstrate the utility of our approach on trainingstandard deep models for semantic instance segmentation and object detection ofcars in outdoor driving scenes. We test the models trained on our augmenteddata on the KITTI 2015 dataset, which we have annotated with pixel-accurateground truth, and on Cityscapes dataset. Our experiments demonstrate thatmodels trained on augmented imagery generalize better than those trained onsynthetic data or models trained on limited amount of annotated real data.

Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes