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Availability of a few, large-size, annotated datasets, like ImageNet, PascalVOC and COCO, has lead deep learning to revolutionize computer vision researchby achieving astonishing results in several vision tasks.We argue that newtools to facilitate generation of annotated datasets may help spreadingdata-driven AI throughout applications and domains. In this work we proposeShooting Labels, the first 3D labeling tool for dense 3D semantic segmentationwhich exploits Virtual Reality to render the labeling task as easy and fun asplaying a video-game. Our tool allows for semantically labeling large scaleenvironments very expeditiously, whatever the nature of the 3D data at hand(e.g. point clouds, mesh). Furthermore, Shooting Labels efficiently integratesmultiusers annotations to improve the labeling accuracy automatically andcompute a label uncertainty map. Besides, within our framework the 3Dannotations can be projected into 2D images, thereby speeding up also anotoriously slow and expensive task such as pixel-wise semantic labeling. Wedemonstrate the accuracy and efficiency of our tool in two different scenarios:an indoor workspace provided by Matterport3D and a large-scale outdoorenvironment reconstructed from 1000+ KITTI images.

Shooting Labels: 3D Semantic Labeling by Virtual Reality