Spatial Understanding from Videos: Structured Prompts Meet Simulation Data

2025-06-04

AI Summary

Current vision-language models (VLMs) exhibit limited 3D spatial reasoning capabilities due to inherent spatial uncertainty and scarcity of real-world, densely annotated 3D scene data—hindering their deployment in embodied tasks such as robot navigation. To address this, we propose SpatialMind, a structured prompting strategy, and ScanForgeQA, a simulation-driven question-answering dataset for 3D spatial understanding. Our approach integrates structured prompt engineering, automated generation of photorealistic 3D simulation environments, and instruction-tuning using synthetic data—enabling zero-architecture-modification adaptation of off-the-shelf VLMs. Evaluated across multiple 3D spatial reasoning benchmarks, our method significantly outperforms standard baselines. Results validate the effectiveness of synergistically combining structured prompting with targeted instruction fine-tuning. This work establishes an interpretable, scalable, and architecture-agnostic pathway for enhancing embodied spatial intelligence in VLMs.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Embodied AI

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Visual-spatial understanding, the ability to infer object relationships and layouts from visual input, is fundamental to downstream tasks such as robotic navigation and embodied interaction. However, existing methods face spatial uncertainty and data scarcity, limiting the 3D spatial reasoning capability of pre-trained vision-language models (VLMs). To address these challenges, we present a unified framework for enhancing 3D spatial reasoning in pre-trained VLMs without modifying their architecture. This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes through an automated construction process designed for fine-tuning. Extensive experiments across multiple benchmarks demonstrate the individual and combined effectiveness of our prompting and fine-tuning strategies, and yield insights that may inspire future research on visual-spatial understanding.

Problem

• Enhancing 3D spatial reasoning in pre-trained vision-language models
• Addressing spatial uncertainty and data scarcity in visual-spatial understanding
• Combining structured prompting and simulation data for improved spatial inference

Innovation

• Structured prompting for interpretable reasoning steps
• Automated dataset from 3D simulation scenes
• Unified framework without modifying VLM architecture