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SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors

Chenyang Ma, Kai Lu, Ta-Ying Cheng, Niki Trigoni, Andrew Markham

TL;DR

This work presents SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner.

Abstract

Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA). However, we believe that higher-level 3D-aware tasks, such as articulating dynamic scene changes and motion planning, require a fundamental and explicit 3D understanding beyond current spatial VQA datasets. In this work, we present SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner. Extensive experiments demonstrate that our spatial reasoning-imbued VLM performs well on various forms of spatial VQA and can extend to help in various downstream robotics tasks such as pick and stack and trajectory planning.

SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors

TL;DR

This work presents SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner.

Abstract

Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA). However, we believe that higher-level 3D-aware tasks, such as articulating dynamic scene changes and motion planning, require a fundamental and explicit 3D understanding beyond current spatial VQA datasets. In this work, we present SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner. Extensive experiments demonstrate that our spatial reasoning-imbued VLM performs well on various forms of spatial VQA and can extend to help in various downstream robotics tasks such as pick and stack and trajectory planning.
Paper Structure (20 sections, 5 equations, 9 figures, 10 tables)

This paper contains 20 sections, 5 equations, 9 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. 2D Image Scene Understanding
  5. Coarse 3D Scene Understanding
  6. Fine-Grained 3D Scene Understanding
  7. Combining External Tools for Downstream Tasks
  8. Experiments
  9. Spatial Visual Question Answering
  10. Robotics Pick and Stack
  11. Discovering and Planning for Robotics Tasks from a Single Image
  12. Ablation Study
  13. Discussion and Conclusion
  14. Overview
  15. Partial 3D Scene Reconstruction Details
  16. ...and 5 more sections

Figures (9)

  • Figure 1: We present SpatialPIN, a framework to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with 3D priors in a zero-shot, training-free manner.
  • Figure 2: SpatialPIN. Our plug-and-play framework is fully modularized and designed for zero-shot deployment. Each module can be easily replaced with the latest updates. Exact prompts for VLMs are in Appendix.
  • Figure 3: Our method of partial 3D scene reconstruction (a). The reconstructed scene (b) and the input image (c) show high alignment.
  • Figure 4: Qualitative examples of spatial VQA. SpatialPIN outputs answers with fine-grained 3D reasoning. Zoom in for better view.
  • Figure 5: Qualitative examples of pick and stack (top) and task trajectory planning (bottom). SpatialPIN successfully outputs picking and stacking policies using spatial reasoning and plans 3D trajectories with geometric awareness to align with task descriptions.
  • ...and 4 more figures