3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera (1910.02527v1)

Abstract: A comprehensive semantic understanding of a scene is important for many applications - but in what space should diverse semantic information (e.g., objects, scene categories, material types, texture, etc.) be grounded and what should be its structure? Aspiring to have one unified structure that hosts diverse types of semantics, we follow the Scene Graph paradigm in 3D, generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e.g., class, material, and other attributes), rooms (e.g., scene category, volume, etc.) and cameras (e.g., location, etc.), as well as the relationships among these entities. However, this process is prohibitively labor heavy if done manually. To alleviate this we devise a semi-automatic framework that employs existing detection methods and enhances them using two main constraints: I. framing of query images sampled on panoramas to maximize the performance of 2D detectors, and II. multi-view consistency enforcement across 2D detections that originate in different camera locations.

• The paper introduces a framework for 3D Scene Graphs that extends traditional 2D representations to capture rich semantic, spatial, and camera data in 3D environments.
• It employs a semi-automated approach using framing and multi-view consistency to overcome occlusions and enhance scene annotation accuracy.
• The work provides enriched datasets and a robust structure that benefits research in robotics, virtual reality, and autonomous navigation.

Overview of "3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera"

The paper presents a methodology for creating a 3D Scene Graph, a comprehensive framework that integrates semantic, spatial, and camera-related information for 3D environments. The proposed framework aims to overcome limitations inherent in grounding semantic information purely in 2D image data by anchoring it within a 3D spatial context. This allows for enhanced stability and robustness in understanding and interacting with 3D spaces.

Key Contributions

1. Extension of Scene Graphs to 3D: The authors extend the concept of 2D scene graphs from prior works such as Visual Genome into 3D space, enabling the integration of detailed semantic and spatial information. This involves representing 3D scenes through a multi-layer graph where nodes can denote objects, rooms, buildings, and camera viewpoints, while edges capture relationships such as part-of, spatial adjacency, and occlusion.
2. Automation of Scene Graph Construction: A novel semi-automatic framework is introduced to manage the construction of 3D Scene Graphs, which mitigates the extensive manual labor typically required in creating such structured representations. Two robustification techniques enhance existing detection methods: framing of query images from panoramas to enhance 2D detectors, and multi-view consistency that aggregates 2D detections from various viewpoints into a coherent 3D model.
3. Data Contribution and Accessibility: The authors augment the Gibson Environment dataset with 3D Scene Graph annotations and provide this enriched dataset publicly. This offers a valuable resource for further research in 3D scene understanding and multi-modal AI applications.

Technical Approach

The methodology involves converting 3D mesh representations of environments into layered graph structures with semantic annotations. By employing 2D detectors such as Mask R-CNN on panoramic images and leveraging geometric consistency across multiple views, the framework achieves a consistent and comprehensive annotation of 3D spaces. This aggregation compensates for common detection errors, such as those arising from occlusions or partial views of objects.

Framing and Voting Mechanism: Detections are optimized using weighted majority voting schemes, which consider the centrality and confidence of detections to produce accurate panoramic semantic maps.

Multi-View Consistency: This ensures the robustness of 3D object representations as the features visible in different panoramic views are aggregated, thus enhancing the computational reliability of the resulting 3D graph.

Implications and Future Directions

The amalgamation of semantic information within a 3D spatial context opens up new avenues for AI applications in robotics, autonomous systems, and virtual reality. The ability to project semantic data into a stable 3D framework allows for more nuanced interaction and understanding systems, accelerating tasks like navigation, search, and automated understanding in complex environments.

Practically, the development of such unified structures will likely enhance the efficiency and performance of visual understanding tasks. Theoretical implications include a deeper exploration of the intersections between computer vision, graph theory, and machine learning.

Potential Future Work:

The methodology could be expanded to include dynamic scenes, incorporating temporal changes and interactive elements, thereby enriching the scope of applications. Moreover, extending this approach outdoors and to larger geographical areas presents opportunities for urban-scale perception systems.

Conclusion

The proposed 3D Scene Graph framework represents an effective strategy for integrating semantics with spatial and camera information in a 3D context. It leverages automation and existing detection tools to minimize manual annotation efforts, thereby facilitating the creation and dissemination of richly annotated datasets. This advancement not only contributes to the field of scene understanding in computer vision but also lays a foundation for further interdisciplinary research and innovation.