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Institut
- Hasso-Plattner-Institut für Digital Engineering gGmbH (19) (entfernen)
Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.
Bridge damage
(2020)
Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements.
Interactive Close-Up Rendering for Detail plus Overview Visualization of 3D Digital Terrain Models
(2019)
This paper presents an interactive rendering technique for detail+overview visualization of 3D digital terrain models using interactive close-ups. A close-up is an alternative presentation of input data varying with respect to geometrical scale, mapping, appearance, as well as Level-of-Detail (LOD) and Level-of-Abstraction (LOA) used. The presented 3D close-up approach enables in-situ comparison of multiple Regionof-Interests (ROIs) simultaneously. We describe a GPU-based rendering technique for the image-synthesis of multiple close-ups in real-time.
The availability of detailed virtual 3D building models including representations of indoor elements, allows for a wide number of applications requiring effective exploration and navigation functionality. Depending on the application context, users should be enabled to focus on specific Objects-of-Interests (OOIs) or important building elements. This requires approaches to filtering building parts as well as techniques to visualize important building objects and their relations. For it, this paper explores the application and combination of interactive rendering techniques as well as their semanticallydriven configuration in the context of 3D indoor models.
A fundamental task in 3D geovisualization and GIS applications is the visualization of vector data that can represent features such as transportation networks or land use coverage. Mapping or draping vector data represented by geometric primitives (e.g., polylines or polygons) to 3D digital elevation or 3D digital terrain models is a challenging task. We present an interactive GPU-based approach that performs geometry-based draping of vector data on per-frame basis using an image-based representation of a 3D digital elevation or terrain model only.
3D point cloud technology facilitates the automated and highly detailed acquisition of real-world environments such as assets, sites, and countries. We present a web-based system for the interactive exploration and inspection of arbitrary large 3D point clouds. Our approach is able to render 3D point clouds with billions of points using spatial data structures and level-of-detail representations. Point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering, e.g., based on semantics. A set of interaction techniques allows users to collaboratively work with the data (e.g., measuring distances and annotating). Additional value is provided by the system’s ability to display additional, context-providing geodata alongside 3D point clouds and to integrate processing and analysis operations. We have evaluated the presented techniques and in case studies and with different data sets from aerial, mobile, and terrestrial acquisition with up to 120 billion points to show their practicality and feasibility.
The rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, and reconstruction of multiple different object types in a 3D point-cloud scene. We present our current research and describe the implementation of these technologies as a web-based application using a services-oriented methodology.
Mixed-projection treemaps
(2017)
This paper presents a novel technique for combining 2D and 2.5D treemaps using multi-perspective views to leverage the advantages of both treemap types. It enables a new form of overview+detail visualization for tree-structured data and contributes new concepts for real-time rendering of and interaction with treemaps. The technique operates by tilting the graphical elements representing inner nodes using affine transformations and animated state transitions. We explain how to mix orthogonal and perspective projections within a single treemap. Finally, we show application examples that benefit from the reduced interaction overhead.
3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications.
Texture mapping is a key technology in computer graphics. For the visual design of 3D scenes, in particular, effective texturing depends significantly on how important contents are expressed, e.g., by preserving global salient structures, and how their depiction is cognitively processed by the user in an application context. Edge-preserving image filtering is one key approach to address these concerns. Much research has focused on applying image filters in a post-process stage to generate artistically stylized depictions. However, these approaches generally do not preserve depth cues, which are important for the perception of 3D visualization (e.g., texture gradient). To this end, filtering is required that processes texture data coherently with respect to linear perspective and spatial relationships. In this work, we present an approach for texturing 3D scenes with perspective coherence by arbitrary image filters. We propose decoupled deferred texturing with (1) caching strategies to interactively perform image filtering prior to texture mapping and (2) for each mipmap level separately to enable a progressive level of abstraction, using (3) direct interaction interfaces to parameterize the visualization according to spatial, semantic, and thematic data. We demonstrate the potentials of our method by several applications using touch or natural language inputs to serve the different interests of users in specific information, including illustrative visualization, focus+context visualization, geometric detail removal, and semantic depth of field. The approach supports frame-to-frame coherence, order-independent transparency, multitexturing, and content-based filtering. In addition, it seamlessly integrates into real-time rendering pipelines and is extensible for custom interaction techniques. (C) 2015 Elsevier Ltd. All rights reserved.