Robust and Explainable 3D Computer Vision

稳健且可解释的 3D 计算机视觉

• 批准号: FT210100268
• 负责人: Prof Ajmal Mian
• 金额: $ 79.71万
• 依托单位: The University of Western Australia
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-03-28 至 2026-03-27
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT210100268
• 关键词: Robust; Explainable; 3D; Computer; Vision

Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.

计算机视觉越来越依靠深度学习，这种学习是脆弱的，不透明的，并且在没有警告的情况下灾难性地失败。该项目的目的是通过在3D几何和图像数据的图表中开发新的理论，层次图简化和专门针对几何图表深度学习设计的新型模块来解决这些问题。使用这些模块，它旨在设计图形卷积网络体系结构，以进行自我监督的学习，这些学习对于失败是可靠的，并为对象检测和场景细分提供了可解释的决策。结果有望在强大的深度学习和受益3D映射，测量，基础设施监测，运输和机器人技术行业方面提高理论。