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

• URL: http://arxiv.org/abs/2403.13438v5
• Date: Wed, 30 Oct 2024 00:47:52 GMT
• Title: SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors
• Authors: Chenyang Ma, Kai Lu, Ta-Ying Cheng, Niki Trigoni, Andrew Markham,
• Abstract summary: Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA) 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. 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.
• Score: 42.85605789984155
• License:
• 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.

Related papers

• Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning [19.399925987942204]
  Vision language models (VLMs) have demonstrated impressive performance across a wide range of downstream tasks. Our evaluation reveals that state-of-the-art VLMs frequently generate implausible and incorrect responses to composite spatial reasoning problems. To address this, we explore an effective approach to enhance 2D spatial reasoning within VLMs by training the model solely on basic spatial capabilities.
  arXiv  Detail & Related papers  (2024-10-21T16:26:09Z)
• LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image [72.14973729674995]
  Current 3D perception methods, particularly small models, struggle with processing logical reasoning, question-answering, and handling open scenario categories. We propose solutions: Spatial-Enhanced Local Feature Mining for better spatial feature extraction, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations.
  arXiv  Detail & Related papers  (2024-08-14T10:00:16Z)
• I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction [32.46674157164291]
  ZeroVLM employs Zero-1-to-3, a 3D reconstruction model for obtaining different views of the input images. Experimental results on four visual spatial reasoning datasets show that our ours achieves up to 19.48% accuracy improvement.
  arXiv  Detail & Related papers  (2024-07-19T09:03:30Z)
• VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs [102.36953558562436]
  Vision language models (VLMs) are an exciting emerging class of language models (LMs) One understudied capability inVLMs is visual spatial planning. Our study introduces a benchmark that evaluates the spatial planning capability in these models in general.
  arXiv  Detail & Related papers  (2024-07-02T00:24:01Z)
• SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models [68.13636352687257]
  We introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts.
  arXiv  Detail & Related papers  (2024-06-03T17:59:06Z)
• OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning [68.45848423501927]
  We propose a holistic framework for strong alignment between agent models and 3D driving tasks. Our framework starts with a novel 3D MLLM architecture that uses sparse queries to lift and compress visual representations into 3D. We propose OmniDrive-nuScenes, a new visual question-answering dataset challenging the true 3D situational awareness of a model.
  arXiv  Detail & Related papers  (2024-05-02T17:59:24Z)
• SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities [59.39858959066982]
  understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. We develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
  arXiv  Detail & Related papers  (2024-01-22T18:01:01Z)
• Exploring and Improving the Spatial Reasoning Abilities of Large Language Models [0.0]
  Large Language Models (LLMs) represent formidable tools for sequence modeling. We investigate the out-of-the-box performance of ChatGPT-3.5, ChatGPT-4 and Llama 2 7B models when confronted with 3D robotic trajectory data. We introduce a novel prefix-based prompting mechanism, which yields a 33% improvement on the 3D trajectory data.
  arXiv  Detail & Related papers  (2023-12-02T07:41:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.