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

点云是感知的3D数据和基础应用程序中最重要的数据表示之一，用于映射，数字工程，数字双胞胎，城市分析，机器人技术，自动驾驶和混合现实。数据采集​​方法和设备（例如摄影测量法和激光雷达）已达到成熟状态。因此，点云数据已广泛可用，也可以以中等成本获取。这是相对缺乏算法可以自动理解和分类3D感应数据的算法的对比的。在过去的十年中，深度学习将2D图像理解的进步推向了与人类绩效的竞争。尽管在2D方面取得了这些进步，但用于自动3D感应的数据理解（例如点云）的技术相对不成熟。基于深度学习的下一代AI有望成为自动化3D点云的理解中的游戏改变者。2.3Aims和Absocivesy1：使用可用的国家映射点云数据建立多域，多域或多国家基线数据集，以基于下一代DL Architectures。有可能进一步采购设备（例如手持式扫描仪）。 Y3：多域点云解释的新型DL框架的开发和文档。基准测试和比较DL体系结构与当前的艺术状态。