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Semantic annotations are vital for training models for object recognition,semantic segmentation or scene understanding. Unfortunately, pixelwiseannotation of images at very large scale is labor-intensive and only littlelabeled data is available, particularly at instance level and for streetscenes. In this paper, we propose to tackle this problem by lifting thesemantic instance labeling task from 2D into 3D. Given reconstructions fromstereo or laser data, we annotate static 3D scene elements with rough boundingprimitives and develop a model which transfers this information into the imagedomain. We leverage our method to obtain 2D labels for a novel suburban videodataset which we have collected, resulting in 400k semantic and instance imageannotations. A comparison of our method to state-of-the-art label transferbaselines reveals that 3D information enables more efficient annotation whileat the same time resulting in improved accuracy and time-coherent labels.

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer