Unified Scene Representation and Reconstruction for 3D Large Language Models (2404.13044v1)

Abstract: Enabling LLMs to interact with 3D environments is challenging. Existing approaches extract point clouds either from ground truth (GT) geometry or 3D scenes reconstructed by auxiliary models. Text-image aligned 2D features from CLIP are then lifted to point clouds, which serve as inputs for LLMs. However, this solution lacks the establishment of 3D point-to-point connections, leading to a deficiency of spatial structure information. Concurrently, the absence of integration and unification between the geometric and semantic representations of the scene culminates in a diminished level of 3D scene understanding. In this paper, we demonstrate the importance of having a unified scene representation and reconstruction framework, which is essential for LLMs in 3D scenes. Specifically, we introduce Uni3DR^2 extracts 3D geometric and semantic aware representation features via the frozen pre-trained 2D foundation models (e.g., CLIP and SAM) and a multi-scale aggregate 3D decoder. Our learned 3D representations not only contribute to the reconstruction process but also provide valuable knowledge for LLMs. Experimental results validate that our Uni3DR^2 yields convincing gains over the baseline on the 3D reconstruction dataset ScanNet (increasing F-Score by +1.8\%). When applied to LLMs, our Uni3DR^2-LLM exhibits superior performance over the baseline on the 3D vision-language understanding dataset ScanQA (increasing BLEU-1 by +4.0\% and +4.2\% on the val set and test set, respectively). Furthermore, it outperforms the state-of-the-art method that uses additional GT point clouds on both ScanQA and 3DMV-VQA.

Enhancing 3D Scene Understanding and Interaction for LLMs via Unified Scene Representation and Reconstruction

Introduction

In the field of AI, interacting comprehensively with 3D environments presents unique challenges. Although LLMs have shown remarkable capabilities in interpreting 1D and 2D data, their application in 3D contexts is often hindered by inadequate representation learning mechanisms. The paper introduces an innovative approach, termed Uni3DR, which advances the idea of unified representation and reconstruction to foster more intuitive interaction of LLMs with 3D environments.

Unified Scene Representation and Reconstruction Framework

Uni3DR integrates a sequence of processes including a 2D encoder, a 3D decoder, and a reconstruction module to transform video inputs into an informative 3D model that LLMs can interpret. The core components are:

• 2D Encoder: Utilizes foundation models like SAM (trained on object-level masks) and CLIP (trained on massive image-text pairs) to extract detailed features from raw images.
• 3D Decoder: Translates the 2D features into structured 3D representations using multi-scale GRU fusion, ensuring spatial coherence and rich semantic content.
• Reconstruction Module: Produces geometrically precise results using lightweight 3D predictions, which then become valuable inputs for subsequent LLM processing.

These elements ensure that the generated 3D models contain both geometric and semantic accuracies essential for effective understanding and interaction by the LLMs.

Experimental Results and Evaluation

The efficacy of Uni3DR was tested against various benchmarks, providing a comprehensive validation:

1. 3D Reconstruction Quality: Compared to existing methods like NeuralRecon, Uni3DR showed a 1.8% higher F-Score on the ScanNet dataset, indicating superior reconstruction capabilities.
2. NLP and Vision-Language Performance: On the ScanQA dataset, Uni3DR-LLM achieved significant improvements over the baseline 3D-LLM model, with +4.0% and +4.2% increases in BLEU-1 scores on the validation and test set respectively. Additionally, it outperformed other methods using extra GT point clouds, providing robust evidence of its enhanced 3D scene understanding capabilities.

Notably, both quantitative and qualitative analyses underscore that Uni3DR enables a more profound and contextually accurate interpretation of 3D scenes by LLMs.

Implications and Future Directions

The introduction of Uni3DR represents a significant step towards bridging the gap between high-level language understanding and intricate 3D scene interpretation. By embedding rich semantic information into systematically structured 3D models, Uni3DR opens avenues for more dynamic and context-aware AI applications in areas such as autonomous navigation, robotic manipulation, and interactive AI training environments.

Future research might explore scaling the Uni3DR framework to handle larger and more complex environments or integrating more advanced LLMs to further enhance the model's interpretative capabilities. Additionally, the extension of such models to real-time applications poses an exciting avenue for practical deployment and utility.

Conclusion

The Uni3DR model not only sets new standards in 3D representation and reconstruction for LLMs but also offers a replicable method that can be tailored for various advanced AI applications requiring robust 3D interactions. As AI continues to evolve, the integration of such sophisticated models will undoubtedly play a pivotal role in shaping future AI capabilities, enabling them to understand and interact with the three-dimensional world in increasingly human-like ways.