3DGraphLLM: Combinando Grafos Semânticos e Modelos de Linguagem Grandes para Compreensão de Cena 3D
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding
Resumo
Summary
AI-Generated Summary
Paper Overview
The paper introduces the 3DGraphLLM method to enhance large language models' performance in 3D vision-language tasks by constructing a learnable representation of a 3D scene graph. The method leverages semantic relationships between objects, outperforming baseline approaches in tasks like 3D referred object grounding and scene captioning on datasets like Multi3DRefer and Scan2Cap.
Core Contribution
- Introduces the 3DGraphLLM method for constructing a learnable representation of a 3D scene graph.
- Utilizes pre-trained encoders for 3D point clouds and semantic relationships to enhance LLM performance.
- Optimizes inference speed by reducing the number of tokens required to describe the scene.
- Demonstrates state-of-the-art quality on popular 3D vision-language datasets.
- Shows significant improvements in accuracy and token efficiency compared to baseline methods.
Research Context
The research addresses the need for improved performance in 3D vision-language tasks by leveraging semantic relationships in a 3D scene graph. It builds upon existing methods by incorporating pre-trained encoders, learnable identifier tokens, and a novel approach to mapping object features to a large language model's token embedding space.
Keywords
3DGraphLLM, Large Language Models (LLMs), 3D Scene Graph, Semantic Relationships, Object Grounding, Scene Captioning, Pre-trained Encoders, Token Efficiency, Inference Speed
Background
The paper focuses on enhancing the performance of large language models in 3D vision-language tasks by constructing a learnable representation of a 3D scene graph. The rationale behind this study lies in the importance of capturing semantic relationships between objects in a scene to improve the accuracy of language model responses.
Research Gap
Existing literature lacks methods that effectively leverage semantic relationships in 3D scenes to enhance large language models' performance in vision-language tasks.
Technical Challenges
- Efficiently representing 3D scene graphs with semantic relationships.
- Mapping object features to a large language model's token embedding space.
- Optimizing inference speed while maintaining accuracy.
- Handling instances where ground-truth data for training is unavailable.
Prior Approaches
Existing solutions in 3D vision-language tasks have not fully utilized semantic relationships in 3D scenes, leading to limitations in accuracy and efficiency.
Methodology
The research methodology involves constructing a learnable representation of a 3D scene graph, leveraging pre-trained encoders for 3D point clouds and semantic relationships. The method optimizes inference speed by reducing token usage and involves joint training of projection layers and the language model for various vision-language tasks.
Theoretical Foundation
The method is based on the construction of a learnable representation of a 3D scene graph using pre-trained encoders and semantic relationships to enhance large language model performance.
Technical Architecture
- Utilizes pre-trained encoders for 3D point clouds and semantic relationships.
- Includes projection layers to map object features and relationships to the language model's token embedding space.
- Involves a three-layer MLP implementation for projection layers.
Implementation Details
- Object proposals represented as point clouds with 6 dimensions for 3D coordinates and RGB color.
- Learnable identifier tokens added to the LLM's vocabulary for object identification.
- Training involves pre-training on ground-truth instance segmentation data and fine-tuning with predicted instance segmentation.
Innovation Points
- Efficiently represents 3D scene graphs with semantic relationships.
- Reduces token usage for scene description, optimizing inference speed.
- Jointly trains projection layers and language model for improved performance.
- Incorporates pre-trained encoders and learnable identifier tokens for enhanced object identification.
Experimental Validation
The experimental validation involves training and evaluating the 3DGraphLLM method on datasets like ScanNet and 3RScan for various 3D vision-language tasks. Metrics such as Acc@0.25, Acc@0.5, F1 score, CIDEr@0.5, and BLEU-4@0.5 are used to evaluate performance.
Setup
- Training on datasets with ground-truth instance segmentation and fine-tuning with predicted segmentation.
- Evaluation on popular 3D vision-language benchmarks like Multi3DRefer and Scan2Cap.
- Utilizes k-nearest neighbor selection with a minimum distance filter for efficient inference.
Metrics
- Evaluation metrics include Acc@0.25, Acc@0.5, F1 score, CIDEr@0.5, and BLEU-4@0.5 for various tasks.
- Metrics used for visual grounding, object descriptions, and scene captioning tasks.
Results
- Outperforms baseline approaches in 3D object grounding and scene captioning tasks.
- Shows promising results in accuracy and token efficiency compared to state-of-the-art methods.
- Demonstrates comparable performance to specialized models like 3D-VisTA, PQ3D, and M3DRef-CLIP.
Comparative Analysis
- Compares the performance of 3DGraphLLM with state-of-the-art approaches for 3D vision-language tasks.
- Shows significant improvements in accuracy and efficiency compared to existing methods.
Impact and Implications
The 3DGraphLLM method presents key findings in enhancing large language models' performance in 3D vision-language tasks. While showing promising results, the method has limitations and suggests future research directions for improving token efficiency and semantic relation generation robustness.
Key Findings
- Outperforms baseline approaches in 3D vision-language tasks.
- Demonstrates state-of-the-art quality on popular datasets.
- Shows significant improvements in accuracy and token efficiency.
- Comparable performance to specialized models in the field.
Limitations
- Requires significant computational resources due to increased edge numbers.
- Integrating spatial relations did not significantly improve performance.
Future Directions
- Focus on reducing token usage for encoding object relationships.
- Improve semantic relation generation robustness.
- Explore methods to enhance inference speed without compromising accuracy.
Practical Significance
The method has practical applications in improving the performance of large language models in 3D vision-language tasks, potentially advancing various fields like robotics, augmented reality, and autonomous systems.