Technology Category
Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.Intelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Large Multimodal Models (LMMs)
Integrating Prior Observations for Incremental 3D Scene Graph Prediction
🤖 AI Summary
Existing 3D Semantic Scene Graph (3DSSG) methods rely on complete scene reconstruction and single-sensor input, limiting their applicability to real-world incremental and dynamic modeling scenarios. To address this, we propose an end-to-end incremental 3DSSG prediction framework. Our approach introduces a heterogeneous graph neural network that directly incorporates historical observations into the message-passing process, enabling joint global–local representation learning. It fuses multimodal inputs—including RGB-D data and textual prompts—and leverages CLIP-based semantic embeddings for cross-modal alignment. Crucially, the method operates without requiring full-scene reconstruction, thereby significantly improving generalization and scalability in partially observable, continuously evolving environments. We validate its effectiveness on standard 3DSSG benchmarks, demonstrating robust performance under incremental observation settings. This work establishes a deployable foundation for long-horizon intelligent interaction through incremental semantic understanding.
Technology Category
Application Category
📝 Abstract
3D semantic scene graphs (3DSSG) provide compact structured representations of environments by explicitly modeling objects, attributes, and relationships. While 3DSSGs have shown promise in robotics and embodied AI, many existing methods rely mainly on sensor data, not integrating further information from semantically rich environments. Additionally, most methods assume access to complete scene reconstructions, limiting their applicability in real-world, incremental settings. This paper introduces a novel heterogeneous graph model for incremental 3DSSG prediction that integrates additional, multi-modal information, such as prior observations, directly into the message-passing process. Utilizing multiple layers, the model flexibly incorporates global and local scene representations without requiring specialized modules or full scene reconstructions. We evaluate our approach on the 3DSSG dataset, showing that GNNs enriched with multi-modal information such as semantic embeddings (e.g., CLIP) and prior observations offer a scalable and generalizable solution for complex, real-world environments. The full source code of the presented architecture will be made available at https://github.com/m4renz/incremental-scene-graph-prediction.
Problem
Research questions and friction points this paper is trying to address.
Incremental 3D scene graph prediction without complete reconstructions
Integrating prior observations and multi-modal information
Scalable 3D semantic reasoning for real-world robotics
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heterogeneous graph model integrates multi-modal information
Layered architecture combines global and local representations
GNNs enriched with semantic embeddings and prior observations
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