Teaching machines to see in 4D

教学机器以 4D 方式观看

• 批准号: 537560-2018
• 负责人: Yi, KwangMooKM
• 金额: $ 5.17万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749767
• 关键词: Teaching; machines; see; 4D

Augmented reality (AR) and mixed reality (MR) are two very recent applications that are drawing much market interest, being the core technology behind flagship features of high-tech products, for example the iPhoneX and Microsoft Hololens. Under the hood of these new technology is the ability of machines to model and track what it is observing. Unfortunately, despite the phenomenal advancements from deep learning, these modeling and tracking methods still rely heavily on decade-old algorithms and human- designed ideas. The recent success of deep learning, exemplified by AlphaGo that surpassed humans, suggests that we can do much better.In this proposal we will teach machines to see in 4D (space and time), by developing learning-based approaches for real-time 4D visual tracking. We will let machines provide much more accurate modeling and tracking results, ultimately resulting in seamless integration of the reality and virtual world, enhanced human machine interaction based on more accurate and detailed gesture recognitions, and allow machines to intelligently interact with the world. All in real-time.The success of this project will push Canada forward and allow Canada to lead the newly emerging industry of AR/MR products and autonomous robotics.

增强现实（AR）和混合现实（MR）是两种非常最新的应用程序，它们引起了极大的市场兴趣，这是高科技产品的旗舰功能背后的核心技术，例如iPhonex和Microsoft Hololens。在这些新技术的引擎盖下，机器能够建模和跟踪其观察到的内容。不幸的是，尽管深度学习的出色发展，这些建模和跟踪方法仍然很大程度上依赖十年历史的算法和人类设计的思想。深度学习的最新成功是由Alphago超越人类的Alphago的，这表明我们可以做得更好。在此提案中，我们将通过开发基于学习的实时4D视觉跟踪的方法来教机器在4D（时空）中看到。我们将让机器提供更准确的建模和跟踪结果，最终导致现实和虚拟世界的无缝整合，基于更准确，更详细的手势识别增强了人类机器互动，并允许机器与世界智能互动。全部实时。该项目的成功将推动加拿大前进，并允许加拿大领导新兴的AR/MR产品和自主机器人技术。