Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning

Scene-LLM: Extending Language Models for 3D Visual Understanding and Reasoning

The paper under review presents Scene-LLM, a novel 3D-visual-language model designed to enhance embodied agents' capabilities within 3D indoor environments by leveraging the reasoning proficiencies of LLMs. Scene-LLM achieves this integration through a hybrid 3D visual feature representation that encompasses dense spatial information and supports scene state updates.

Key Contributions

The paper makes several notable contributions to the field of 3D visual understanding and reasoning:

1. Hybrid 3D Visual Feature Representation: Scene-LLM employs a hybrid representation that includes both egocentric and scene-level 3D information. This dual approach is essential for interactive planning, enabling both localized and global environmental understanding.
2. Efficient Projection Layer: The model uses a projection layer to map the hybrid 3D visual features into the pre-trained textual embedding space effectively. This mapping is crucial for the interpretation of 3D visual information using LLMs.
3. Dataset Generation: The authors generated a large-scale dataset comprising approximately 190,000 3D-frame-language pairs and 500,000 3D-scene-language pairs. This dataset is pivotal for aligning 3D visual information with textual modalities and fine-tuning the model for various tasks.
4. Two-Stage Training Strategy: The training involves an initial pretraining phase where the model aligns 3D visual concepts with textual features followed by a fine-tuning phase that refines the model’s responses to user instructions.
5. Empirical Evaluations: Scene-LLM was evaluated on multiple benchmarks including ScanQA, SQA3D, and ALFRED. It demonstrated state-of-the-art performance in dense captioning, question answering, and interactive planning tasks.

Methodology

Scene-LLM's methodological framework involves several critical components:

3D Visual Feature Extraction

The model extracts pixel-wise features from images and aggregates these into 3D point sets forming a 3D frame. For more comprehensive scene understanding, Scene-LLM uses a hybrid point-voxel representation that efficiently handles dense spatial information while supporting interactive updates.

3D-Visual-Language Alignment

The approach involves dual-stage training:

• Stage 1: Pretraining - Focuses on aligning 3D visual concepts with textual features using 3D frame-language data, helping the model comprehend both egocentric and scene-centric perspectives.
• Stage 2: Fine-tuning - Involves using both 3D frame and 3D scene-language data to refine the model's response generation capabilities.

Inference Strategy

For static tasks, the model processes 3D visual data and generates responses to user instructions. In interactive scenarios, it adopts a two-step process involving egocentric and scene-level updates to handle dynamic changes in the environment.

Empirical Results

Scene-LLM was rigorously tested on several benchmarks with the following outcomes:

1. 3D-VQA Benchmarks: On ScanQA and SQA3D benchmarks, Scene-LLM outperformed existing models on most metrics, highlighting its robust 3D scene understanding and reasoning abilities. Specifically, it achieved impressive scores in metrics like Exact Match (EM) and BLEU.
2. Interactive Planning Benchmark (ALFRED): When integrated into the ALFRED benchmark, Scene-LLM showed superior high-level planning accuracy (HLP), outperforming other methods that use step-by-step instructions. This underscores Scene-LLM's proficiency in task decomposition and high-level planning without needing additional visual feature extractors.
3. Dense Caption Generation: On the Scan2Cap benchmark, Scene-LLM produced state-of-the-art results across all metrics, including CIDEr, BLEU-4, METEOR, and ROUGE. This indicates Scene-LLM's aptitude for detailed 3D scene description.

Ablation Studies

Comprehensive ablation studies validated several design choices:

• 3D Visual Representation: The proposed hybrid point-voxel representation was shown to be highly effective.
• Training Strategy: Using frame data in pretraining was found to accelerate convergence and enhance conceptual understanding.
• Voxel Grid Resolution: Higher voxel resolution significantly improved performance on 3D QA benchmarks, indicating the importance of detailed spatial information.

Discussion and Future Work

While Scene-LLM advances the state-of-the-art in 3D visual understanding and reasoning, there are inherent limitations such as the token length constraint of LLama2 and challenges in processing dynamic scenes without state detectors. Future work might focus on addressing these limitations by incorporating LLMs capable of handling longer token inputs, developing robust state detection mechanisms, and integrating geometric features to complement dense spatial information.

Conclusion

Scene-LLM represents a substantial step forward in 3D visual understanding and reasoning, particularly in interactive 3D environments. By seamlessly integrating dense spatial information with the deep reasoning capabilities of LLMs, it paves the way for more sophisticated and nuanced interactions in indoor settings, expanding the horizon for practical applications of embodied AI.