This study introduces SpatialPrompting, a novel framework that harnesses the emergent reasoning capabilities of off-the-shelf multimodal large language models to achieve zero-shot spatial reasoning in three-dimensional (3D) environments. Unlike existing methods that rely on expensive 3D-specific fine-tuning with specialized 3D inputs such as point clouds or voxel-based features, SpatialPrompting employs a keyframe-driven prompt generation strategy. This framework uses metrics such as vision-language similarity, Mahalanobis distance, field of view, and image sharpness to select a diverse and informative set of keyframes from image sequences and then integrates them with corresponding camera pose data to effectively abstract spatial relationships and infer complex 3D structures. The proposed framework not only establishes a new paradigm for flexible spatial reasoning that utilizes intuitive visual and positional cues but also achieves state-of-the-art zero-shot performance on benchmark datasets, such as ScanQA and SQA3D, across several metrics. The proposed method effectively eliminates the need for specialized 3D inputs and fine-tuning, offering a simpler and more scalable alternative to conventional approaches.

本研究提出了SpatialPrompting，一个新框架，利用现成的多模态大型语言模型的推理能力，实现三维环境中的零-shot空间推理。该框架通过关键帧驱动的提示生成策略，有效地抽象出空间关系并推断复杂的三维结构，创造出一种灵活的空间推理新范式，并在多个基准数据集上实现了最先进的零-shot性能。

SpatialPrompting：基于关键帧的零-shot空间推理与现成的多模态大型语言模型