CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
arXiv:2606.07529v1 Announce Type: cross Abstract: Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we der
The increasing integration of Large Language Models (LLMs) with 3D vision tasks necessitates more efficient spatial reasoning methods to manage computational complexity and ensure prompt reliability.
This development enhances the practical application of LLMs in complex 3D environments, enabling more robust and resource-efficient AI systems for spatial understanding and interaction.
The ability to prune scene graphs without sacrificing critical relational information will make 3D-VL tasks more feasible and scalable for LLMs, moving beyond current limitations of computational cost and accuracy.
- · AI developers working on robotics and augmented reality
- · Companies building 3D vision-language systems
- · Researchers focused on LLM efficiency and spatial reasoning
- · Inefficient 3D-VL methods
- · Hardware providers unprepared for optimized AI workloads
More sophisticated and computationally efficient spatial reasoning in LLMs for 3D environments.
Accelerated development of AI-driven applications requiring deep understanding of physical spaces, such as advanced robotics, virtual reality, and autonomous systems.
Enhanced human-AI interaction in 3D digital and physical realms, leading to new forms of collaborative tasks and automated problem-solving.
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