MSQA - Large-scale multimodal 3D contextual reasoning dataset

What is MSQA?

MSQA (Multi-modal Situated Question Answering) is a large-scale multimodal situated reasoning dataset designed to enhance the understanding and reasoning capabilities of embodied AI agents in 3D scenes. The dataset contains 251K question-answer pairs covering nine question categories, collected from real-world 3D scenes using 3D scene graphs and a visual-language model. MSQA reduces ambiguity of unimodal inputs by using interleaved multimodal inputs of text, images, and point clouds. It introduces the MSNN (Multi-modal Next-step Navigation) benchmark to evaluate the model's ability to navigate between contexts, contributing to the development of more powerful situated reasoning models and advancing 3D scene understanding technology.

Main functions of MSQA

• Multimodal situational reasoningMSQA provides a dataset containing 251K question-answer pairs, covering 9 different question categories, involving complex scenarios and object modalities in 3D scenes.
• Data modality diversityIt supports multiple data modalities such as text, images, and point clouds, providing a more comprehensive contextual description and reducing the limitations and ambiguities of single-modal input.
• Evaluate model performanceDesign two benchmark tasks, MSQA and MSNN, to evaluate and compare the contextual reasoning and navigation capabilities of different models in 3D scenes.
• Promote AI researchBased on providing large-scale multimodal datasets, MSQA has driven research progress in the fields of embodied AI and 3D scene understanding.
• Pre-training and model developmentThe MSQA dataset serves as pre-training material, helping to develop and optimize more powerful contextual reasoning models.

MSQA Technical Principles

• Data collection and generationAutomatically and scalably collect data in real-world 3D scenes using 3D scene graphs and visual-language models (VLMs).
• Multimodal input settingsIntroducing interleaved multimodal inputs, combining text, image, and point cloud data, provides more accurate context and problem descriptions.
• Situational awareness modelingIntegrating input data from different modalities improves the model's ability to perceive and understand the context.
• Evaluation benchmark designWe designed two benchmark tests, MSQA and MSNN, to comprehensively evaluate the model's multimodal understanding and contextual reasoning capabilities, targeting contextual question answering and next-step navigation tasks respectively.
• Model Evaluation and AnalysisExperiments were conducted on MSQA and MSNN to analyze the limitations of existing models and explore the importance of handling multimodal inputs and contextual modeling.

MSQA project address

• Project official website:msr3d.github.io
• arXiv technical paper:https://arxiv.org/pdf/2409.02389

MSQA Application Scenarios

• Intelligent navigation systemIn indoor or outdoor environments, it helps develop intelligent systems that understand complex spatial relationships and provide navigation instructions.
• Augmented Reality (AR) and Virtual Reality (VR)In AR and VR applications, it provides a deeper understanding and interaction with the virtual environment, enhancing the user experience.
• Robot InteractionTo enable robots to understand and respond to questions about their surroundings, thereby improving their ability to manipulate and interact in complex 3D spaces.
• autonomous vehiclesIt assists autonomous vehicles in understanding traffic scenarios, providing more accurate decision support, and coping with complex road conditions.
• Smart assistants and chatbots: Understand the user's 3D spatial query and provide more accurate and context-sensitive answers.