The remarkable potential of multi-modal large language models (MLLMs) in
comprehending both vision and language information has been widely
acknowledged. However, the scarcity of 3D scenes-language pairs in comparison
to their 2D counterparts, coupled with the inadequacy of existing approaches in
understanding of 3D scenes by LLMs, poses a significant challenge. In response,
we collect and construct an extensive dataset comprising 75K
instruction-response pairs tailored for 3D scenes. This dataset addresses tasks
related to 3D VQA, 3D grounding, and 3D conversation. To further enhance the
integration of 3D spatial information into LLMs, we introduce a novel and
efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment
stage between 3D scenes and language and extends the instruction prompt with
the 3D modality information including the entire scene and segmented objects.
We evaluate the effectiveness of our method across diverse tasks in the 3D
scene domain and find that our approach serves as a strategic means to enrich
LLMs’ comprehension of the 3D world. Our code is available at
https://github.com/staymylove/3DMIT.
The Potential of Multi-Modal Large Language Models in Understanding 3D Scenes
The integration of vision and language information has long been a goal in the field of multimedia information systems. The ability to comprehend and interpret both visual and textual content opens up a wide range of possibilities for applications such as animations, artificial reality, augmented reality, and virtual realities.
In this article, we explore the remarkable potential of multi-modal large language models (MLLMs) in comprehending 3D scenes. While MLLMs have shown great promise in understanding 2D images and text, the scarcity of 3D scenes-language pairs and the existing challenges in understanding 3D scenes have posed significant obstacles.
To address this challenge, the authors of the article have collected and constructed an extensive dataset comprising 75K instruction-response pairs specifically tailored for 3D scenes. This dataset covers tasks related to 3D visual question answering (3D VQA), 3D grounding, and 3D conversation.
In addition to the dataset, the authors propose a novel paradigm called 3DMIT (3D Modality Information Tuning) to enhance the integration of 3D spatial information into MLLMs. This paradigm eliminates the need for an alignment stage between 3D scenes and language by extending the instruction prompt with 3D modality information, including the entire scene and segmented objects.
The effectiveness of the proposed method is evaluated across diverse tasks in the 3D scene domain, and the results show that this approach significantly enhances MLLMs’ comprehension of the 3D world. By bridging the gap between vision and language, MLLMs can now better understand and interpret complex 3D scenes, leading to improved performance in various applications.
This work highlights the multi-disciplinary nature of the concepts discussed. The integration of vision, language, and spatial information requires expertise from various fields, including computer vision, natural language processing, and graphics.
In the wider field of multimedia information systems, this research contributes to the development of more advanced animations, artificial reality, augmented reality, and virtual realities. By improving the capabilities of MLLMs to understand 3D scenes, we can expect enhanced user experiences and more immersive virtual environments. This has implications for industries such as gaming, virtual simulations, and virtual tours.
In conclusion, the potential of multi-modal large language models in comprehending 3D scenes is a significant advancement in the field of multimedia information systems. The combination of vision and language information, coupled with novel techniques like 3DMIT, opens up new possibilities for a wide range of applications. By addressing the challenges in understanding 3D scenes, this research paves the way for more sophisticated and interactive multimedia experiences.
Code Availability: The code for the proposed method is available at https://github.com/staymylove/3DMIT.