Jürgen Döllner

3D point clouds used in geospatial applications typically contain billions of points. Processing 3D point clouds of this size as a whole with deep learning models requires computational resources (e.g., GPU memory) that are usually not available. To obtain 3D point clouds that can be processed by deep learning models, sampling methods that produce local subsets of large-scale 3D point clouds with a smaller extent or lower density are essential. Nonetheless, the impact of different input sampling methods on the semantic segmentation performance of deep learning models has received little attention so far. In this paper, we compare three widely used input sampling techniques (random sampling, farthest point sampling, and grid sampling) concerning the semantic segmentation performance of different deep learning architectures, using inputs of different spatial extents. We consider both indoor and outdoor scenarios, using the Stanford Large-Scale 3D Indoor Spaces and Paris-CARLA-3D datasets as reference datasets. We find that random and grid sampling outperform farthest point sampling in terms of segmentation performance, with mean intersection-over-union scores of approximately 0.6, while random sampling displays the fastest execution time. For indoor scenarios, using input 3D point clouds with a small spatial extent (i.e., 1m) yields the best results. For outdoor scenarios, similar performance is obtained for all tested input extents. In an additional experiment, we evaluate a curvature-weighted sampling approach to test whether geometric features derived from 3D point clouds can guide the selection of more informative input points for deep learning models. However, we find that using curvature as a sampling criterion decreases the segmentation performance, indicating a mismatch between the expected relevance of high-curvature points (e.g., points representing object borders) and the internally learned features of the deep learning models.

Various web-based image-editing tools and web-based collaborative tools exist in isolation. Research focusing to bridge the gap between these two domains is sparse. We respond to the above and develop prototype groupware for real-time collaborative editing of raster images in a web browser. To better understand the requirements, we conduct a preliminary user study and establish communication and synchronization as key elements. The existing groupware for text documents, presentations, and vector graphics handles the above through well-established techniques. However, those cannot be extended as it is for raster graphics manipulation. To this end, we develop a document model that is maintained by a server and is delivered and synchronized to multiple clients. Our prototypical implementation is based on a scalable client-server architecture: using WebGL for interactive browser-based rendering and WebSocket connections to maintain synchronization. We evaluate our work qualitatively through a post-deployment user study for three different scenarios.

One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management.

Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.