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.
@article{arxiv.1910.05021,
title = {Shooting Labels: 3D Semantic Labeling by Virtual Reality},
author = {Pierluigi Zama Ramirez and Claudio Paternesi and Luca De Luigi and Luigi Lella and Daniele De Gregorio and Luigi Di Stefano},
journal= {arXiv preprint arXiv:1910.05021},
year = {2020}
}