3DONT

3D Ontologies for point cloud representation, management and querying

Reimagining 3D Point Clouds

3DOnt introduces a groundbreaking approach to managing 3D point cloud data by integrating semantic technologies and knowledge graphs. Rather than treating 3D data as unstructured geometry, 3DOnt represents point clouds as 3D Graphs, enriching every point with meaningful, interpretable information. This enables professionals in fields like cultural heritage, smart cities, and environmental monitoring to explore, analyze, and understand 3D datasets through intuitive interfaces—no programming required.

Representing 3D data:

Ontology, Semantics and Mereology

Semantics at the Core

At the heart of 3DOnt is the “3D Graph”—a semantic, spatially structured representation that transforms raw point clouds into meaningful knowledge graphs. Each point is enriched with ontological concepts, allowing the system to understand and organize complex entities like “roof,” “column,” or “degradation risk.” This semantic layer doesn’t just describe what’s visible—it embeds contextual relationships, such as part-whole hierarchies or material dependencies, enabling automated reasoning and interpretation.

Smart, Open-Vocabulary Classification

3DOnt uses 2D imagery and projection techniques to automatically classify 3D point clouds with semantic labels from customizable ontologies. Its open-vocabulary system supports multi-label segmentation (points can belong to multiple objects) and adapts to different domains—no predefined class set or manual annotation required. This capability accelerates the structuring of 3D data and makes semantic enrichment scalable, explainable, and deeply integrable with downstream analysis.

Structuring Objects Through Mereological Relationships

3DOnt organizes 3D data into meaningful objects by leveraging mereological (part-whole) relationships encoded in domain ontologies. Instead of treating the point cloud as an undifferentiated mass, the framework identifies and links components—such as walls, beams, or columns—to their higher-level structures, like rooms or entire buildings. This semantic decomposition enables precise queries, targeted analysis, and reasoning over structural hierarchies. Users can explore not only where things are, but how they are composed and interconnected, unlocking deeper insight into the geometry and function of complex systems.

From Data to Knowledge:

Data Collection, Digital Twinning and Reasoning

Multimodal Data Integration through Referential Entities

The 3D Graph format allows for using entities - as nodes of the graph - as referential joints that anchor and unify all collected data across modalities. Each entity serves as a semantic hub, connecting geometric features, sensor observations, historical records, and inferred attributes within a coherent, queryable structure. This enables a multimodal and temporally-aware representation of reality, where heterogeneous data streams are continuously aligned to their referents. By organizing incoming data around ontological entities, 3DOnt ensures consistency, traceability, and semantic interoperability—laying the foundation for advanced reasoning, monitoring, and insight generation.

Heating Propension Index - calculated in 3DOnt by reasoning over:

Materials' Specific Heat Capacity
RGB

Flooding Risk Index - calculated in 3DOnt by reasoning over:

Materials' Permeability to Water
Geometries (Concavities: Steepness, Depth)
Proximity to Rivers

Explainable Reasoning for Insight and Automation

3DOnt integrates a powerful reasoning engine that goes beyond data visualization, enabling automated inference of high-level properties from geometric, semantic, and physical data. By leveraging the underlying ontological structure, the system can detect patterns, classify elements, infer missing relationships, and compute properties such as environmental risk. Reasoning in 3DOnt is transparent and explainable—every inference can be traced back to its sources and rules—giving users full control over how knowledge is derived and applied. This capability transforms raw 3D data into actionable insight, supporting decision-making, diagnostics, and intelligent monitoring.

Dew Formation Risk Index - calculated in 3DOnt by reasoning over:

Sensor-Measured Temperature
Sensor-Measured Humidity
Verticality
Proximity to Floor
Temporal Permanence of Critical Factors

Digital Twinning with Semantic-Geometric Intelligence

3DOnt redefines digital twinning by transforming 3D point clouds into rich, semantically structured digital replicas that evolve over time. Unlike conventional models, 3DOnt’s twins are built on a semantic-geometric foundation: every point is part of a knowledge graph that links spatial coordinates with domain-specific meaning, physical properties, and part-whole relationships. Real-time sensor data, 2D imagery, and historical records can all be integrated into this structure, enabling dynamic updates and multi-temporal analysis. Thanks to its open and explainable architecture, 3DOnt allows users to compute and monitor high-level properties—such as the dew formation index on surfaces—directly within the digital twin. These semantic twins are not only visually accurate, but also interpretable, queryable, and adaptable to changing conditions.

User-Friendly and Expressive Querying:

Natural Language, SPARQL and Visual

Natural Language Querying

With 3DOnt, users can interact with complex 3D and semantic data through simple, natural language queries. Whether asking for "all surfaces with high dew formation risk" or "columns belonging to degraded buildings," the system parses the question, interprets its meaning using the underlying ontology, and returns precise results—visually and semantically. This intuitive interface opens advanced querying to non-technical users, bridging the gap between expert knowledge and raw data.

SPARQL for Expert Control

For advanced users and developers, 3DOnt offers full support for SPARQL—the standard query language for RDF graphs. This allows fine-grained access to the underlying semantic model, enabling complex data retrieval, filtering, and transformation. Users can write precise queries to extract, combine, and analyze spatial, temporal, and semantic information across the 3D graph, supporting advanced workflows, research, and integration with external tools.

Interactive 3D Viewer

The 3DOnt platform features a powerful web-based 3D viewer that seamlessly integrates geometry with semantic information. Users can explore annotated point clouds, inspect object hierarchies, view real-time sensor overlays, and highlight elements based on query results—all within an intuitive interface. Every point, object, or property in the viewer is linked to its semantic context, making the 3D model not just a visual reference, but a fully navigable knowledge space.

Some Videos!

Related publications:

Codiglione, M., Beber, R., and Remondino, F., 2024: Leveraging ontology for enhanced queries and analyses of urban point clouds. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W8-2024, 101–108
Codiglione, M., Mazzacca, G., Remondino, F., 2024: Dimensional discoveries: unveiling the potential of 3D heritage point clouds with a robust ontology framework. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W4-2024, 119–125