2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)最新文献_第8页

Eye Tracking Data Collection Protocol for VR for Remotely Located Subjects using Blockchain and Smart Contracts 使用区块链和智能合约的远程定位对象的VR眼动追踪数据收集协议

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2020-10-23 DOI: 10.1109/AIVR50618.2020.00083

Efe Bozkir, Shahram Eivazi, Mete Akgün, Enkelejda Kasneci

引用次数: 2

Comfort of Aircraft Seats for Customers of Size using Digital Human Model in Virtual Reality 使用虚拟现实中的数字人体模型为不同尺寸的客户提供飞机座椅舒适度

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2020-10-16 DOI: 10.1109/AIVR50618.2020.00045

Sara Panicker, T. Huysmans

引用次数: 1

Style-transfer GANs for bridging the domain gap in synthetic pose estimator training 在合成姿态估计器训练中弥合领域差距的风格转移 GANs

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2020-04-28 DOI: 10.1109/AIVR50618.2020.00039

Pavel Rojtberg, Thomas Pollabauer, Arjan Kuijper

{"title":"Style-transfer GANs for bridging the domain gap in synthetic pose estimator training","authors":"Pavel Rojtberg, Thomas Pollabauer, Arjan Kuijper","doi":"10.1109/AIVR50618.2020.00039","DOIUrl":"https://doi.org/10.1109/AIVR50618.2020.00039","url":null,"abstract":"Given the dependency of current CNN architectures on a large training set, the possibility of using synthetic data is alluring as it allows generating a virtually infinite amount of labeled training data. However, producing such data is a nontrivial task as current CNN architectures are sensitive to the domain gap between real and synthetic data.We propose to adopt general-purpose GAN models for pixellevel image translation, allowing to formulate the domain gap itself as a learning problem. The obtained models are then used either during training or inference to bridge the domain gap. Here, we focus on training the single-stage YOLO6D [20] object pose estimator on synthetic CAD geometry only, where not even approximate surface information is available. When employing paired GAN models, we use an edge-based intermediate domain and introduce different mappings to represent the unknown surface properties.Our evaluation shows a considerable improvement in model performance when compared to a model trained with the same degree of domain randomization, while requiring only very little additional effort.","PeriodicalId":348199,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126253251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Shooting Labels: 3D Semantic Labeling by Virtual Reality 拍摄标签:虚拟现实的3D语义标签

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2019-10-11 DOI: 10.1109/AIVR50618.2020.00027

Pierluigi Zama Ramirez, Claudio Paternesi, Daniele De Gregorio, L. D. Stefano

{"title":"Shooting Labels: 3D Semantic Labeling by Virtual Reality","authors":"Pierluigi Zama Ramirez, Claudio Paternesi, Daniele De Gregorio, L. D. Stefano","doi":"10.1109/AIVR50618.2020.00027","DOIUrl":"https://doi.org/10.1109/AIVR50618.2020.00027","url":null,"abstract":"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.","PeriodicalId":348199,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125138750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12