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之一，是地图绘制、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应用的基础。摄影测量和激光雷达等数据采集方法和设备已经达到成熟状态。因此，点云数据是广泛可用的，或者可以以适度的成本获得。这与相对缺乏可以以高可靠性或准确性自动理解和分类3D感测数据的算法形成对比。在过去的十年中，深度学习推动了2D图像理解的进步，使其与人类的表现相媲美。尽管在2D方面取得了这些进步，但用于自动3D感测数据理解（诸如点云）的技术相对不成熟。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目标和目的Y1：使用可用的国家测绘点云数据建立多域、多传感器或多国基线数据集，以基准测试下一代深度学习架构。Y2：使用OS（Street Drone）和UCL（Robin）的移动的地图系统以及可能的其他采集设备（例如手持式SLAM扫描仪）采集多域点云数据集。Y3：开发和记录用于多域点云解释的新型DL框架。将DL架构与当前技术水平进行基准测试和比较。Y 4：完成论文。2.3.1范围内：使用机器学习对来自各种来源的3D点云进行分类研究通过ML2.3.2重新标记点云分类的方法超出范围：从点云创建多边形/多边形要素几何