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