3d LLM Injecting The 3d World Into Large Language Models Arxiv 2307 12981v2

3D-LLM: Injecting the 3D World into Large Language Models

One-line decision

Use this skill when you want to create 3D-language instruction data for training LLMs to understand and reason about 3D environments. Avoid it when you only work with 2D images.

Skill metadata

• Skill type: 3d-language-instruction-data
• Paper kind: operational-method
• Actionability: high
• Evidence quality: full_paper

Goal

Create 3D-language instruction data by rendering multi-view images from 3D scenes and using them with GPT-4 to generate 3D-grounded instruction data for training 3D-LLMs.

Problem signature

• Modality: 3D scenes with language instructions for 3D understanding.
• Data state: 3D scenes converted to multi-view images with LLM-generated instructions.
• Scale regime: 300K 3D-language instruction data points.
• Model requirement: 3D feature extractor + LLM for 3D understanding.

Use when

• You want to add 3D understanding to LLMs.
• You can render multi-view images from 3D scenes.
• You need 3D-grounded instruction data.

Do not use when

• 2D understanding is sufficient.
• You do not have 3D scene data.
• 3D understanding is not needed.

Required inputs

• 3d_scenes: 3D scene data (ScanNet, Objaverse, etc.).
• multi_view_renderer: Renderer for creating multi-view images.
• instruction_generator: GPT-4 for generating 3D instructions from multi-view context.

Optional inputs

• 3d_features: Pre-computed 3D features for efficiency.

Outputs

• 3d_instruction_data: 300K 3D-language instruction data points.
• 3d_llm: LLM with 3D spatial understanding.

Assumptions and prerequisites

• Multi-view images capture 3D spatial information.
• GPT-4 can generate 3D-grounded instructions from multi-view context.
• 3D instruction data enables spatial reasoning in LLMs.

Procedure

1. Render multi-view images Action: Render multiple views of 3D scenes. Why: Multi-view captures 3D spatial information. Note: See paper for details.
2. Generate 3D instructions with GPT-4 Action: Use GPT-4 with multi-view context to create 3D instructions. Why: Generates spatially grounded instruction data. Note: See paper for details.
3. Train 3D-LLM Action: Train an LLM on 3D-language instruction data. Why: Adds 3D understanding to the LLM. Note: See paper for details.
4. Evaluate 3D understanding Action: Test on 3D QA, navigation, and grounding tasks. Why: Validates 3D spatial reasoning. Note: See paper for details.

Parameters to set

• num_views — Role: Views per 3D scene. How to set: 8-16 views for coverage. Default/range: 8-16. Effect: More views capture more spatial information.
• instruction_count — Role: Total 3D instructions. How to set: 300K for diverse coverage. Default/range: 300K. Effect: More data improves 3D understanding.

Validation checks

• The model should answer 3D spatial questions correctly.
• Multi-view features should capture 3D structure.
• 3D instruction data should improve over 2D-only data.

Failure modes

• Multi-view images may not fully capture 3D structure.
• GPT-4 may generate spatially inaccurate instructions.
• 3D understanding may be limited by view count.

Adaptation notes for VLM training

• 3D-LLM extends VLM capability to 3D spatial understanding.
• The multi-view instruction generation approach is reusable.
• Combine with 2D instruction data for comprehensive training.

Implementation notes

• Use efficient multi-view rendering.
• Validate spatial accuracy of generated instructions.
• Evaluate on 3D-specific benchmarks.

Evidence from the paper

• 3D-LLM creates 300K 3D-language instruction data from multi-view rendering.
• GPT-4 effectively generates 3D-grounded instructions from multi-view context.
• 3D instruction data enables spatial reasoning in LLMs.
• The approach adds 3D understanding to standard LLM architectures.

Source paper

• Title: 3D-LLM: Injecting the 3D World into Large Language Models
• Year: 2023
• Venue: NeurIPS
• Paper ID: arxiv-2307.12981v2
• URL: http://arxiv.org/abs/2307.12981v2
• arXiv ID: 2307.12981v2