SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities

• URL: http://arxiv.org/abs/2401.12168v1
• Date: Mon, 22 Jan 2024 18:01:01 GMT
• Title: SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
• Authors: Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Danny Driess, Pete Florence, Dorsa Sadigh, Leonidas Guibas, Fei Xia
• Abstract summary: 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.
• Score: 59.39858959066982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still lack capabilities in 3D spatial reasoning, such as recognizing quantitative relationships of physical objects like distances or size differences. We hypothesize that VLMs' limited spatial reasoning capability is due to the lack of 3D spatial knowledge in training data and aim to solve this problem by training VLMs with Internet-scale spatial reasoning data. To this end, we present a system to facilitate this approach. We first develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. We then investigate various factors in the training recipe, including data quality, training pipeline, and VLM architecture. Our work features the first internet-scale 3D spatial reasoning dataset in metric space. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA. Finally, we demonstrate that this VLM unlocks novel downstream applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capability. Project website: https://spatial-vlm.github.io/

Related papers

• SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data [7.142118464319378]
  Vision-language models (VLMs) work well in tasks ranging from image captioning to visual question answering (VQA) We find that spatial relations are generally rare in widely used VL datasets, with only a few being well represented. We construct a synthetic VQA dataset focused on spatial reasoning generated from hyper-detailed image descriptions.
  arXiv  Detail & Related papers  (2025-04-29T11:18:38Z)
• The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? [42.3970767778131]
  3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some promising results, the role of point clouds in 3D spatial reasoning remains under-explored. We comprehensively evaluate and analyze these models to answer the research question: textitDoes point cloud truly boost the spatial reasoning capacities of 3D LLMs?
  arXiv  Detail & Related papers  (2025-04-06T16:38:48Z)
• NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving [10.41584658117874]
  We propose NuScenes-SpatialQA, the first large-scale ground-truth-based Question-Answer (QA) benchmark designed to evaluate the spatial understanding and reasoning capabilities of Vision-Language Models (VLMs) in autonomous driving. Built upon the NuScenes dataset, the benchmark is constructed through an automated 3D scene graph generation pipeline and a QA generation pipeline. Using this benchmark, we conduct extensive experiments on diverse VLMs, including both general and spatial-enhanced models, providing the first comprehensive evaluation of their spatial capabilities in autonomous driving.
  arXiv  Detail & Related papers  (2025-04-04T04:43:10Z)
• MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs [13.678235444299286]
  Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-quality 3D scene data with open-set annotations to introduce 1) a novel supervised fine-tuning dataset and 2) a new evaluation benchmark, focused on indoor scenes.
  arXiv  Detail & Related papers  (2025-03-17T12:34:22Z)
• 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)
• Adapting a Foundation Model for Space-based Tasks [16.81793096235458]
  In the future of space robotics, we see three core challenges which motivate the use of a foundation model adapted to space-based applications. In this work, we demonstrate that 1) existing vision-language models are deficient visual reasoners in space-based applications, and 2) fine-tuning a vision-language model on extraterrestrial data significantly improves the quality of responses.
  arXiv  Detail & Related papers  (2024-08-12T05:07:24Z)
• SpatialBot: Precise Spatial Understanding with Vision Language Models [12.67089704185187]
  Vision Language Models (VLMs) have achieved impressive performance in 2D image understanding. They are still struggling with spatial understanding which is the foundation of Embodied AI. In this paper, we propose SpatialBot for better spatial understanding by feeding both RGB and depth images.
  arXiv  Detail & Related papers  (2024-06-19T15:41:30Z)
• GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs [3.2688425993442696]
  The ability to understand and reason about spatial relationships between objects in images is an important component of visual reasoning. We extend the previously released What'sUp dataset and propose a novel comprehensive evaluation for spatial relationship understanding.
  arXiv  Detail & Related papers  (2024-06-19T06:15:26Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan. The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• 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)
• Language-Image Models with 3D Understanding [59.499585515469974]
  We develop a large-scale pre-training dataset for 2D and 3D called LV3D. Next, we introduce a new MLLM named Cube-LLM and pre-train it on LV3D. We show that pure data scaling makes a strong 3D perception capability without 3D specific architectural design or training objective.
  arXiv  Detail & Related papers  (2024-05-06T17:57:27Z)
• 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)
• SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors [42.85605789984155]
  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.
  arXiv  Detail & Related papers  (2024-03-18T17:38:29Z)
• VIPHY: Probing "Visible" Physical Commonsense Knowledge [22.00069189468524]
  Vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks. We evaluate their ability to acquire "visible" physical knowledge. Our results indicate a severe gap between model and human performance.
  arXiv  Detail & Related papers  (2022-09-15T02:06:25Z)
• SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations [85.38562724999898]
  We propose a 2D Image and 3D Point cloud Unsupervised pre-training strategy, called SimIPU. Specifically, we develop a multi-modal contrastive learning framework that consists of an intra-modal spatial perception module and an inter-modal feature interaction module. To the best of our knowledge, this is the first study to explore contrastive learning pre-training strategies for outdoor multi-modal datasets.
  arXiv  Detail & Related papers  (2021-12-09T03:27:00Z)

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.