Source-linked AI summary
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
Zehan Wang, Haifeng Huang, Yang Zhao, Ziang Zhang, Zhou Zhao
TL;DR
Existing 3D scene-understanding methods focus on specific tasks rather than general dialogue, while scarce 3D scene-text data makes alignment and spatially unambiguous interaction difficult. Chat-3D aligns 3D features with LLMs through three-stage training and object-centric instructions, demonstrating universal dialogue and spatial reasoning, with the training scheme outperforming a prior two-stage method by 8.6 points.
Problem
Existing 3D scene-understanding methods address specific downstream tasks, while limited 3D scene-text data and object ambiguity challenge general dialogue systems.
Method
Chat-3D combines pre-trained 3D representations with LLM reasoning and conversation abilities using three-stage alignment, object-centric instructions, and an object-centric prompt.
Results
Chat-3D demonstrates universal dialogue and spatial reasoning for 3D scenes, while its three-stage training scheme outperforms a prior two-stage method by 8.6 points.
Takeaways & Limitations
The system provides a user-friendly universal dialogue approach that supports diverse object-focused interactions and detailed scene reasoning.
Abstract
from arXiv · showhide
3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstream tasks, which hinders their practicality in real-world applications. This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes. Specifically, we align 3D representations into the feature space of LLMs, thus enabling LLMs to perceive the 3D world. Given the scarcity of 3D scene-text data, we propose a three-stage training strategy to efficiently utilize the available data for better alignment. To enhance the reasoning ability and develop a user-friendly interaction scheme, we further construct a high-quality object-centric 3D instruction dataset and design an associated object-centric prompt. Our experiments show that Chat-3D achieves an impressive ability to comprehend diverse instructions for 3D scenes, engage in intricate spatial reasoning, and incorporate external knowledge into its responses. Chat-3D achieves a 75.6% relative score compared with GPT-4 on the constructed instruction dataset.
1 Introduction
Chat-3D addresses the lack of universal 3D-scene dialogue by combining 3D perception with LLM reasoning and conversation. It uses data-efficient staged training and object-centric interaction to support spatially grounded dialogue.
- Limited 3D scene-text data makes conventional two-stage multimodal-LLM training less effective for aligning 3D representations with LLM feature spaces.
- Its three-stage training progresses from object-attribute alignment to scene-level spatial relations and instruction tuning, improving dialogue quality with limited data.
- Chat-3D builds a universal dialogue system by combining pre-trained 3D visual representations with LLM reasoning and open-domain conversation.
- The object-centric dataset covers attributes, positions, relations, functionalities, placement suggestions, and detailed descriptions, while its prompt enables targeted interaction.
- Experiments show universal dialogue and spatial-reasoning capabilities, with quantitative evaluation of the training scheme and instruction dataset.
2 Related Work
Prior work develops 3D representations and task-specific 3D-language systems, while multimodal LLMs extend language-model reasoning to visual modalities. These strands motivate Chat-3D’s object-level scene representation and universal dialogue goal.
- 3D Representation Learning: 3D representation methods learn robust point-cloud features through self-supervision or cross-modal alignment, providing detailed object information for scene understanding.
- 3D-Language Tasks: Existing 3D-language tasks include captioning, visual question answering, and situated question answering, with increasing emphasis on spatial context and agent situation.
- Multi-modal Large Language Models: Multimodal LLMs typically align visual features with language models using large-scale paired data before instruction tuning for perception and reasoning.
3 Methods
Chat-3D segments scenes into objects, enriches object features with attributes, maps them into an LLM space, and models inter-object relations. A three-stage training strategy and object-centric prompt support interaction under limited 3D-language data.
- 3.1 Architecture: Chat-3D segments each scene into objects, extracts 3D features, adds color, size, and location attributes, and projects them into the pretrained LLM’s word-embedding space.
- 3.1 Architecture: A relation module lets object features interact, supplying additional information about complex relations within the scene.
- 3.1 Architecture: The object-centric prompt marks a selected target object separately from the remaining scene objects, enabling responses grounded in the chosen object and instructions.
- Training Strategy: Sparse 3D scene-text data motivates more efficient alignment than the large-scale image- and video-text data used by prior two-stage methods.
- Training Strategy: The three stages align individual objects, integrate whole-scene relations, and instruction-tune the model for diverse responses and spatial reasoning.
4 Object-centric Instruction Dataset
The paper constructs an object-centric 3D instruction dataset that expands sparse scene annotations into descriptive captions and diverse conversations for improving 3D reasoning and interaction.
- Object-centric Instruction Dataset: The dataset construction targets reasoning about complex object relationships and interactions between agents and scenes.
- Object-centric Instruction Dataset: The dataset uses ChatGPT to generate detailed object-centric captions and multi-turn dialogues from ScanRefer annotations and textualized 3D scenes.The textualized scenes include target-object categories and XYZ coordinates for nearby objects.
- Object-centric Descriptive Captions: Object-centric captions summarize object attributes, spatial relationships, and interactions within a 3D scene.
- Object-centric Conversations: Object-centric conversations are generated in a self-questioning and self-answering format to support diverse instructions and general dialogue.
5 Experiments
Experiments evaluate Chat-3D through quantitative scoring and qualitative comparisons, showing benefits from the three-stage scheme, object-centric data, and explicit 3D spatial reasoning.
- 5.2 Quantitative Analysis: The evaluation scores generated responses from 1 to 10 using helpfulness, relevance, accuracy, and detail across randomly selected ScanRefer validation scenes.
- 5.2 Quantitative Analysis: 8.6 points: the three-stage training scheme outperforms the previous two-stage method in the low-resource setting.Using all instruction data achieves the highest overall score across conversation and detailed-caption tests.
- 5.3 Qualitative Comparisons & Analysis: Chat-3D accurately counts, recognizes, and localizes objects, while also reasoning about object purpose and spatial relationships.These capabilities are illustrated through conversations involving commonly seen objects such as tables, chairs, beds, and cabinets.
- 5.3 Qualitative Comparisons & Analysis: Chat-3D identifies identical monitor sizes and surrounding-object relationships more accurately than the compared 2D multimodal LLMs.The comparison attributes the 2D models’ errors to limitations in depth and perspective understanding.
6 Conclusion
Chat-3D combines pretrained 3D perception with LLM reasoning and conversation to support universal dialogue, spatial reasoning, and external-knowledge enhancement for 3D scenes.
- 6 Conclusion: Chat-3D is presented as the first universal dialogue system for 3D scenes, combining pretrained 3D visual perception with LLM reasoning and open-domain conversation.
- 6 Conclusion: Its three-stage training scheme progressively moves from individual object attributes to complex spatial relations to address limited 3D data.
- 6 Conclusion: The object-centric instruction dataset and prompt support user-friendly interaction with diverse 3D-scene instructions.