SpatialPrompting: Keyframe-driven Zero-Shot Spatial Reasoning with Off-the-Shelf Multimodal Large Language Models
Shun Taguchi, Hideki Deguchi, Takumi Hamazaki, Hiroyuki Sakai·May 08, 2025
Summary
SpatialPrompting leverages off-the-shelf LLMs for 3D spatial reasoning, excelling on benchmarks without specialized inputs. It selects keyframes based on vision-language similarity, sharpness, and field of view, achieving state-of-the-art performance. Ablation studies confirm the importance of its components. SpatialPrompting optimizes scene coverage and model performance across configurations, offering a simpler, scalable alternative. It balances image quantity and API costs/latency in spatial inference, optimizing results at 30 images. A sensitivity analysis identifies optimal parameters for best performance.
Introduction
Background
Overview of 3D spatial reasoning challenges
Importance of off-the-shelf Large Language Models (LLMs) in AI applications
Objective
To introduce SpatialPrompting, a method that enhances 3D spatial reasoning using off-the-shelf LLMs without specialized inputs
To highlight the method's performance on benchmarks and its optimization across configurations
Method
Data Collection
Selection of keyframes based on criteria: vision-language similarity, sharpness, and field of view
Data Preprocessing
Explanation of preprocessing steps to prepare data for LLMs
Model Training and Optimization
Description of how SpatialPrompting leverages off-the-shelf LLMs for 3D spatial reasoning
Discussion on how the method optimizes scene coverage and model performance across different configurations
Cost and Latency Management
Analysis of how SpatialPrompting balances image quantity and API costs/latency in spatial inference
Explanation of the optimization process at 30 images for best results
Sensitivity Analysis
Identification of optimal parameters for SpatialPrompting to achieve the best performance
Results
Benchmark Performance
Presentation of SpatialPrompting's state-of-the-art performance on various benchmarks
Ablation Studies
Detailed analysis of ablation studies confirming the importance of each component in SpatialPrompting
Scalability and Efficiency
Discussion on the method's scalability and efficiency in handling different spatial reasoning tasks
Conclusion
Summary of Contributions
Recap of SpatialPrompting's key contributions to 3D spatial reasoning
Future Work
Suggestions for future research directions to further enhance SpatialPrompting's capabilities
Implications
Discussion on the broader implications of SpatialPrompting for the field of AI and spatial reasoning applications
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
What parameters are identified as optimal in the sensitivity analysis for SpatialPrompting?
How does SpatialPrompting balance image quantity with API costs and latency?
How does SpatialPrompting utilize vision-language similarity to select keyframes?
What are the critical components identified in the ablation studies of SpatialPrompting?