Rico Richter

Out-of-core real-time visualization of massive 3D point clouds

This paper presents a point-based rendering approach to visualize massive sets of 3D points in real-time. In many disciplines such as architecture, engineering, and archeology LiDAR technology is used to capture sites and landscapes; the resulting massive 3D point clouds pose challenges for traditional storage, processing, and presentation techniques. The available hardware resources of CPU and GPU are limited, and the 3D point cloud data exceeds available memory size in general. Hence out-of-core strategies are required to overcome the limit of memory. We discuss concepts and implementations of rendering algorithms and interaction techniques that make out-of-core real-time visualization and exploration of massive 3D point clouds feasible. We demonstrate with our implementation real-time visualization of arbitrarily sized 3D point clouds with current PC hardware using a spatial data structure in combination with a point-based rendering algorithm. A rendering front is used to increase the performance taking into account user interaction as well as available hardware resources. Furthermore, we evaluate our approach, describe its characteristics, and report on applications.

Object class segmentation of massive 3D point clouds of urban areas using point cloud topology

A large number of remote-sensing techniques and image-based photogrammetric approaches allow an efficient generation of massive 3D point clouds of our physical environment. The efficient processing, analysis, exploration, and visualization of massive 3D point clouds constitute challenging tasks for applications, systems, and workflows in disciplines such as urban planning, environmental monitoring, disaster management, and homeland security. We present an approach to segment massive 3D point clouds according to object classes of virtual urban environments including terrain, building, vegetation, water, and infrastructure. The classification relies on analysing the point cloud topology; it does not require per-point attributes or representative training data. The approach is based on an iterative multi-pass processing scheme, where each pass focuses on different topological features and considers already detected object classes from previous passes. To cope with the massive amount of data, out-of-core spatial data structures and graphics processing unit (GPU)-accelerated algorithms are utilized. Classification results are discussed based on a massive 3D point cloud with almost 5 billion points of a city. The results indicate that object-class-enriched 3D point clouds can substantially improve analysis algorithms and applications as well as enhance visualization techniques.

Out‐of‐Core GPU‐based Change Detection in Massive 3D Point Clouds

If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out-of-core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point-based rendering technique adapted for attributed 3D point clouds, to enable effective out-of-core real-time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.

Out-of-Core Visualization of Classified 3D Point Clouds

3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. We present a novel, interactive out-of-core rendering technique for massive 3D point clouds based on a layered, multi-resolution kd-tree, whereby point-based rendering techniques are selected according to each point’s classification (e.g., vegetation, buildings, terrain). The classification-dependent rendering leads to an improved visual representation, enhances recognition of objects within 3D point cloud depictions, and facilitates visual filtering and highlighting. To interactively explore objects, structures, and relations represented by 3D point clouds, our technique provides efficient means for an instantaneous, ad hoc visualization compared to approaches that visualize 3D point clouds by deriving mesh-based 3D models. We have evaluated our approach for massive laser scan datasets of urban areas. The results show the scalability of the technique and how different configurations allow for designing task and domain-specific analysis and inspection tools.

Interactive and View-Dependent See-Through Lenses for Massive 3D Point Clouds

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.

Service-Oriented Processing and Analysis of Massive Point Clouds in Geoinformation Management

Today, landscapes, cities, and infrastructure networks are commonly captured at regular intervals using LiDAR or image-based remote sensing technologies. The resulting point clouds, representing digital snapshots of the reality, are used for a growing number of applications, such as urban development, environmental monitoring, and disaster management. Multi-temporal point clouds, i.e., 4D point clouds, result from scanning the same site at different points in time and open up new ways to automate common geoinformation management workflows, e.g., updating and maintaining existing geodata such as models of terrain, infrastructure, building, and vegetation. However, existing GIS are often limited by processing strategies and storage capabilities that generally do not scale for massive point clouds containing several terabytes of data. We demonstrate and discuss techniques to manage, process, analyze, and provide large-scale, distributed 4D point clouds. All techniques have been implemented in a system that follows service-oriented design principles, thus, maximizing its interoperability and allowing for a seamless integration into existing workflows and systems. A modular service-oriented processing pipeline is presented that uses out-of-core and GPU-based processing approaches to efficiently handle massive 4D point clouds and to reduce processing times significantly. With respect to the provision of analysis results, we present web-based visualization techniques that apply real-time rendering algorithms and suitable interaction metaphors. Hence, users can explore, inspect, and analyze arbitrary large and dense point clouds. The approach is evaluated based on several real-world applications and datasets featuring different densities and characteristics. Results show that it enables the management, processing, analysis, and distribution of massive 4D point clouds as required by a growing number of applications and systems.

COMPARATIVE VISUALIZATION OF BIM GEOMETRY AND CORRESPONDING POINT CLOUDS

We present a set of techniques for the combined and comparative visualization of 3D model geometry extracted from Building Information Models (BIM) and corresponding point clouds. It addresses the steady need to validate, update and combine BIM, in particular based on in-situ captured point clouds, throughout the whole lifecycle of buildings and facilities. To assess the present as-built interior and exterior in comparison to the as-designed or as-documented building representations, our techniques allow for deviation analysis and visualization, which serve as an effective method for enhancing stakeholder engagement. For example, Facility Management (FM) stakeholders can use deviation analysis and visualization to identify, inspect and monitor any spatial alterations both for interior and exterior building parts. Visualized instantaneous deviations can inform stakeholders of further need for investigation; they may not even have architecture, engineering and construction (AEC) expertise or access to BIM software. We describe a prototypical implementation that demonstrates the application of comparative deviation analysis and visualization. Finally, we discuss how the visualization output can provide a tool for a variety of stakeholders to improve applications and workflows for FM.

A service-oriented approach for classifying 3D points clouds by example of office furniture classification

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.

A scalable webGL-based approach for visualizing massive 3D point clouds using semantics-dependent rendering techniques

3D point cloud technology facilitates the automated and highly detailed digital acquisition of real-world environments such as assets, sites, cities, and countries; the acquired 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. In this paper, we present a web-based system for the interactive and collaborative exploration and inspection of arbitrary large 3D point clouds. Our approach is based on standard WebGL on the client side and is able to render 3D point clouds with billions of points. It uses spatial data structures and level-of-detail representations to manage the 3D point cloud data and to deploy out-of-core and web-based rendering concepts. By providing functionality for both, thin-client and thick-client applications, the system scales for client devices that are vastly different in computing capabilities. Different 3D point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering and highlighting, e.g., based on per-point surface categories or temporal information. A set of interaction techniques allows users to collaboratively work with the data, e.g., by measuring distances and areas, by annotating, or by selecting and extracting data subsets. Additional value is provided by the systems ability to display additional, context-providing geodata alongside 3D point clouds and to integrate task-specific processing and analysis operations. We have evaluated the presented techniques and the prototype system with different data sets from aerial, mobile, and terrestrial acquisition campaigns with up to 120 billion points to show their practicality and feasibility.