AI For Understanding Multi-domain Point Clouds

点击翻译按钮获取中文摘要

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