SpatialStack：用于3D VLM空间推理的分层几何-语言融合
SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning
AI总结：
本文提出SpatialStack框架，通过分层融合视觉、几何和语言表征，提升3D空间推理能力，实验表明其在多个基准上表现优异。
摘要：
大型视觉-语言模型（VLMs）在可靠的3D空间推理方面仍面临挑战，这限制了具身和物理AI系统的核心能力。现有方法无法捕捉细粒度的3D几何和空间关系。本文提出SpatialStack，一种通用的分层融合框架，通过逐步对齐视觉、几何和语言表征，提升空间理解能力。基于此框架，开发了VLM-SpatialStack，实现了多个3D空间推理基准的最先进性能。实验表明，多级融合策略有效增强3D理解并泛化到多样化的空间推理任务。
英文摘要：
Large vision-language models (VLMs) still struggle with reliable 3D spatial reasoning, a core capability for embodied and physical AI systems. This limitation arises from their inability to capture fine-grained 3D geometry and spatial relationships. While recent efforts have introduced multi-view geometry transformers into VLMs, they typically fuse only the deep-layer features from vision and geometry encoders, discarding rich hierarchical signals and creating a fundamental bottleneck for spatial understanding. To overcome this, we propose SpatialStack, a general hierarchical fusion framework that progressively aligns vision, geometry, and language representations across the model hierarchy. Moving beyond conventional late-stage vision-geometry fusion, SpatialStack stacks and synchronizes multi-level geometric features with the language backbone, enabling the model to capture both local geometric precision and global contextual semantics. Building upon this framework, we develop VLM-SpatialStack, a model that achieves state-of-the-art performance on multiple 3D spatial reasoning benchmarks. Extensive experiments and ablations demonstrate that our multi-level fusion strategy consistently enhances 3D understanding and generalizes robustly across diverse spatial reasoning tasks, establishing SpatialStack as an effective and extensible design paradigm for vision-language-geometry integration in next-generation multimodal physical AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
