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3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding (2401.03201v2)

Published 6 Jan 2024 in cs.CV and cs.MM

Abstract: The remarkable potential of multi-modal LLMs (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.

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Authors (7)
  1. Zeju Li (27 papers)
  2. Chao Zhang (907 papers)
  3. Xiaoyan Wang (27 papers)
  4. Ruilong Ren (1 paper)
  5. Yifan Xu (92 papers)
  6. Ruifei Ma (3 papers)
  7. Xiangde Liu (3 papers)
Citations (13)
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Summary

Overview of 3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding

The paper "3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding" by Zeju Li et al. presents a novel approach to enhancing the understanding of 3D scenes by LLMs. This is particularly relevant given the acknowledged potential of multi-modal LLMs (MLLMs), which integrate visual and language data. However, the challenge of aligning 3D spatial information with language remains significant due to the relative scarcity of 3D scene-language datasets. The authors address this with the creation of an expansive dataset and a new instruction tuning paradigm.

Dataset Construction

The authors have constructed a comprehensive dataset consisting of 75,000 instruction-response pairs specifically designed for 3D scenes. These pairs encompass tasks such as 3D Visual Question Answering (VQA), 3D Captioning, 3D Grounding, and 3D Conversations. The dataset is a significant contribution as it extends existing collections like ScanNet and ScanRefer, thereby providing a rich resource for training models on multi-task 3D scene understanding.

Method: 3DMIT

3DMIT introduces a prompt tuning paradigm that incorporates 3D modality information directly into LLMs without requiring a separate alignment stage. This contrasts with previous methods that often involved time-consuming stages of aligning 3D visual features with text embeddings. The method comprises the following steps:

  1. Scene Encoding: A pre-trained scene encoder is used to extract global scene features from the point cloud data.
  2. Object Segmentation and Encoding: The scene is segmented, and a pre-trained 3D encoder extracts features for individual objects within the scene.
  3. Prompt Construction: Visual features and textual prompts are concatenated to form 3D multi-modal prompts.
  4. Fine-tuning: The LLMs are fine-tuned using these 3D multi-modal prompts, thus enabling them to better understand and reason about 3D scenes.

Evaluation and Results

The authors evaluated 3DMIT using several traditional 3D-language downstream tasks: 3D VQA on the ScanQA validation dataset, and 3D Grounding on the ScanRefer validation dataset. The performance of 3DMIT was benchmarked against various baselines, including traditional 3D-LLMs that require alignment stages and those that do not.

3D VQA Results:

  • The proposed method significantly outperformed LLMs without alignment stages, such as LAMM and zero-shot LLaVA, across various metrics including BLEU, ROUGE, and CIDEr.
  • While it did not surpass all performance metrics compared to expert models, it demonstrated comparable results, particularly in BLEU-4 scoring.

3D Grounding Results:

  • The paper illustrated that while traditional models like ScanRefer demonstrated superior bounding box accuracy, 3DMIT performed robustly in object identification tasks, highlighting its effectiveness in specific 3D understanding scenarios.

Implications and Future Developments

The practical implications of 3DMIT are manifold:

  • Efficiency: By eliminating the alignment stage, 3DMIT reduces the complexity and computational overhead traditionally associated with multi-modal training.
  • Adaptability: The method shows promising transferability across different LLMs and MLLMs, raising possibilities for diverse applications in AI-driven scene understanding, robotics, and beyond.

From a theoretical perspective, this work suggests that direct infusion of 3D data into LLMs can yield efficient and effective understanding without the need for laborious alignment processes. Future developments could explore the integration of more complex datasets and refinement of the multi-modal prompts to further improve the models' capabilities in detailed spatial reasoning tasks.

Conclusion

The paper by Zeju Li et al. offers a crucial step forward in the optimization of LLMs for 3D scene understanding. The 3DMIT framework, with its efficient prompt tuning paradigm, presents a compelling approach that bypasses the need for alignment stages, thus simplifying the integration of 3D modality information into LLMs. This work opens up avenues for more streamlined, scalable multimodal comprehension models in the AI landscape.

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GitHub

  1. GitHub - staymylove/3DMIT: Code of 3DMIT: 3D MULTI-MODAL INSTRUCTION TUNING FOR SCENE UNDERSTANDING (25 stars)