AI For Understanding Multi-domain Point Clouds

用于理解多域点云的人工智能

• 批准号: 2897909
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2897909
• 关键词: AI; Understanding; Multi; domain; Point

Point clouds are one of the most significant data representations for sensed 3D data and underpin applications in mapping, digital engineering, digital twins, urban analytics, robotics, autonomous driving, and mixed reality. The data acquisition methods and devices, such as photogrammetry and LIDAR, have reached a mature state. Therefore, point cloud data is widely available or can be acquired at moderate cost. This is contrasted by a relative lack of algorithms that can automatically understand and classify 3D sensed data with high reliability or accuracy. Over the past decade, deep learning has driven progress in 2D image understanding to rival human performance. Despite these advancements in 2D, techniques for automatic 3D sensed data understanding, such as point clouds, are comparatively immature. Next-generation AI based on Deep Learning promises to be a game changer in automated 3D point cloud understanding.2.3Aims and objectivesY1: Using available national mapping point cloud data establish multi-domain, multi-sensor or multi-country baseline dataset to benchmark next-gen DL architectures.Y2: Acquire multi-domain point cloud dataset using mobile mapping systems from OS (Street Drone) and UCL (Robin) and potentially further acquisition devices(e.g. handheld SLAM scanners). Y3: Development and Documentation of novel DL framework for multi-domain point cloud interpretation. Benchmarking and comparing the DL architecture with the current state of art.Y4: Thesis completion.2.3.1In scope:Classifying 3D point clouds from various sources using machine learningInvestigation of relabelling approaches to point cloud classification by ML2.3.2Out of Scope:Creation of polygon/polyherdral feature geometries from pointclouds

点云是感知 3D 数据最重要的数据表示形式之一，支撑着测绘、数字工程、数字孪生、城市分析、机器人、自动驾驶和混合现实等领域的应用。摄影测量、激光雷达等数据采集方法和设备已达到成熟状态。因此，点云数据可以广泛使用或者可以以适中的成本获取。与此形成鲜明对比的是，相对缺乏能够以高可靠性或准确性自动理解和分类 3D 传感数据的算法。在过去的十年中，深度学习推动了二维图像理解的进步，达到了与人类相媲美的水平。尽管 2D 领域取得了这些进步，但自动 3D 传感数据理解技术（例如点云）相对不成熟。基于深度学习的下一代人工智能有望成为自动化 3D 点云理解的游戏规则改变者。2.3目的和目标Y1：使用可用的国家测绘点云数据建立多域、多传感器或多国家基线数据集，以对下一代 DL 架构进行基准测试。Y2：使用 OS（Street Drone）和 UCL（Robin）的移动测绘系统获取多域点云数据集和 潜在的更多采集设备（例如手持式 SLAM 扫描仪）。 Y3：用于多域点云解释的新型深度学习框架的开发和文档记录。对深度学习架构与当前技术水平进行基准测试和比较。Y4：论文完成。2.3.1范围内：使用机器学习对来自各种来源的 3D 点云进行分类研究 ML2.3.2 点云分类的重新标记方法超出范围：从点云创建多边形/多面体特征几何形状