1. Introduction
Recent advancements in MLLMs have opened new avenues for human-computer interaction, yet their capacity for precise 3D spatial reasoning, crucial for tasks like 3D visual grounding, remains limited. Existing MLLMs often infer spatial relationships implicitly from 2D projections or struggle with ambiguous instructions in complex 3D environments, leading to suboptimal performance in accurately identifying specific objects based on their spatial attributes. This work addresses this limitation by proposing a novel framework. Models used include S^2-MLLM, baseline MLLMs, and 3D vision-language models for comparison.
2. Related Work
Previous research has explored enhancing MLLMs with visual understanding, including efforts in 2D visual grounding and object detection using multimodal inputs. While some models attempt to incorporate 3D information through point clouds or voxel representations, they often lack explicit mechanisms for robust spatial reasoning, particularly concerning relative positions and geometric constraints. This section reviews existing MLLMs, dedicated 3D vision-language models, and approaches to integrate spatial knowledge, highlighting their strengths and the remaining challenges that S^2-MLLM aims to overcome.
3. Methodology
S^2-MLLM integrates a novel structural guidance module into a transformer-based MLLM architecture, specifically designed to process and reason over 3D scene graphs or explicit structural representations. The methodology involves a multi-stage training process where the model first learns to embed 3D scene features and linguistic queries, then leverages a graph attention network to incorporate object-object spatial relationships as structural guidance. This mechanism enables the MLLM to explicitly reason about spatial arrangements, orientations, and connections, thereby improving its ability to accurately ground objects in complex 3D scenes.
4. Experimental Results
Experiments were conducted on several challenging 3D visual grounding datasets, demonstrating the superior performance of S^2-MLLM compared to state-of-the-art baselines. The model achieved significant improvements across various metrics, including accuracy and recall, particularly in scenarios requiring fine-grained spatial understanding and intricate object relationship parsing. For instance, on the ReferIt3D dataset, S^2-MLLM surpassed previous methods by an average of 5-8% in grounding accuracy, showcasing the effectiveness of structural guidance.
| Model | ReferIt3D (Accuracy %) | ScanRefer (Accuracy %) | 3DSSG-Grounding (Accuracy %) |
|---|---|---|---|
| Baseline MLLM (Vanilla) | 48.5 | 52.1 | 45.8 |
| MLLM + Simple 3D Encoder | 53.2 | 56.7 | 50.1 |
| State-of-the-Art MLLM | 58.9 | 62.5 | 55.3 |
| S^2-MLLM (Ours) | 64.2 | 68.9 | 61.5 |
5. Discussion
The experimental results confirm that incorporating explicit structural guidance is highly effective in boosting the spatial reasoning capabilities of MLLMs for 3D visual grounding. The S^2-MLLM framework not only improves grounding accuracy but also enhances the interpretability of spatial reasoning by making explicit use of object relationships. These findings suggest that future MLLM architectures for 3D understanding should prioritize mechanisms for structured spatial knowledge integration, paving the way for more robust and reliable real-world applications in robotics, augmented reality, and complex scene analysis.