"BIM-to-Scan" for Scan-to-BIM: Generating Realistic Synthetic Ground Truth Point Clouds based on Industrial 3D Models
Florian Noichl, Alexander Braun, André Borrmann
Abstract
In the field of Scan-to-BIM, recent developments achieve promising results in accuracy and flexibility, leveraging tools from the field of deep learning for semantic segmentation of raw point cloud data. Those methods demand large-scale, domain-specific datasets for training. Promising ideas to fulfill this need use primitive synthetic point cloud data, which predominantly lack distinct point cloud properties, such as missing patches due to occlusions in the scene. To solve this issue, we use a specialized laser scan simulation tool from the domain of Geosciences in a toolchain that allows generating realistic ground truth data based on 3D models.
Topics & Concepts
Point cloudToolchainGround truthComputer scienceSegmentationField (mathematics)Domain (mathematical analysis)Artificial intelligenceFlexibility (engineering)Point (geometry)LidarComputer visionRemote sensingSoftwareGeometryProgramming languageGeologyStatisticsMathematicsMathematical analysisPure mathematics3D Surveying and Cultural HeritageRemote Sensing and LiDAR Applications3D Shape Modeling and Analysis