Shooting Labels: 3D Semantic Labeling by Virtual Reality
| Autor: | Luigi Lella, Luigi Di Stefano, Luca De Luigi, Claudio Paternesi, Pierluigi Zama Ramirez, Daniele De Gregorio |
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| Přispěvatelé: | Pierluigi Zama Ramirez, Claudio Paternesi, Luigi Di Lella, Daniele De Gregorio, Luigi Di Stefano |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Point cloud ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Virtual reality Machine learning computer.software_genre Semantics Annotation Tool Task (project management) 020901 industrial engineering & automation Data driven AI 0202 electrical engineering electronic engineering information engineering Segmentation computer.programming_language business.industry Deep learning Virtual Reality Pascal (programming language) 020201 artificial intelligence & image processing Semantic Segmentation Artificial intelligence business computer |
| Zdroj: | AIVR |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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