FloorMaterialAnalyzer/开发文档3

Material Intelligence Platform v3

Version: 3.0

Status: Design

1. Objective

The goal of Version 3 is no longer feature extraction.

Version 3 focuses on transforming Material Assets into an intelligent knowledge base that can directly support AI image generation, material retrieval, quality assurance, and future recommendation systems.

Material Analyzer is considered feature-complete.

Version 3 extends it into a Material Intelligence Platform.

2. Architecture
Crawler

↓

Material Analyzer (Completed)

↓

Material Asset Library

↓

Material Intelligence

├── Prompt Builder
├── Material Knowledge Base
├── Similarity Search
├── Material Ground Truth
├── Vision Provider
└── Dataset Builder

↓

Gemini Image Renderer

↓

Material QA (Future)
3. Replace Vision Provider

Current implementation:

Rule Provider

↓

HTTP Provider

Upgrade to Provider Architecture.

vision/

    internvl3/

    florence2/

    qwen2_5vl/

    gemini/

    gpt4o/

Every provider implements

AnalyzeMaterial(image) -> MaterialSemantic

Analyzer must not depend on any specific model.

4. Default Vision Model

Default local model

InternVL3

Reason

Fully offline
Reproducible
No API cost
Fine-grained material understanding
Future LoRA support

Gemini becomes optional.

Gemini is no longer the default analyzer.

Gemini is recommended only for image generation.

5. Semantic Fusion

Instead of trusting one model.

Support multiple providers.

Example

InternVL3

↓

Semantic A

Florence2

↓

Semantic B

↓

Fusion

↓

Final Semantic

Fusion strategy

voting
confidence weighting
field-level merge

Output

semantic.json
6. Material Fingerprint

Generate a readable fingerprint.

Example

{
    "brightness":0.71,
    "contrast":0.18,
    "saturation":0.14,
    "variation":0.22,
    "texture_entropy":0.64,
    "texture_frequency":0.39,
    "orientation":89,
    "dominant_lab":[67,-1,8]
}

Purpose

Prompt Builder
QA
Recommendation
Search

Unlike embedding,

Fingerprint is human-readable.

7. Prompt Builder

New module.

Input

Material Asset

Output

Prompt Block

Example

Material Constraints

Species:
European Oak

Surface:
Low Satin

Variation:
Medium

Gloss:
Low

Keep all material properties identical to the reference texture.

Do not alter species,
finish,
grain,
or gloss.

The rendering pipeline should consume Prompt Blocks instead of manually assembled prompts.

8. Ground Truth Builder

Convert low-level features into canonical material definitions.

Example

Analyzer

↓

Ground Truth Builder

↓

ground_truth.json

Purpose

Separate computer vision features from rendering constraints.

9. Material Knowledge Base

Generate

knowledge.db

Store

SKU

↓

Embedding

↓

Semantic

↓

Fingerprint

Support

search
recommendation
clustering
analytics
10. Similarity Graph

Build

KNN

↓

Material Graph

Example

Bruce Cinnamon

↓

Most Similar

Mohawk Brown Oak

0.96

Output

similarity.json
11. Prompt Dataset

Automatically generate

prompt_dataset.json

Example

{
    "sku":"67907_847",

    "system_prompt":"...",

    "material_prompt":"...",

    "negative_prompt":"..."
}

This dataset becomes the standard prompt source for Gemini.

12. Embedding Index

Generate

faiss.index

Purpose

Nearest-neighbor search

Duplicate detection

Recommendation

Visual search

13. Statistics Center

Generate

statistics.json

Examples

Species Distribution

Brightness Distribution

Material Distribution

Color Distribution

Gloss Distribution

Embedding PCA

Cluster Statistics

14. Asset Integrity

Upgrade manifest.

Current

files

New

SHA256

File Size

Created Time

Analyzer Version

Feature Version

Every asset must pass integrity verification.

15. Regression Test

Every Analyzer update automatically compares

Old Asset

↓

New Asset

Compare

Brightness

Histogram

Embedding

Semantic

Drift

Generate

regression_report.json
16. Vision Benchmark

Evaluate every Vision Provider.

Example

InternVL3

Accuracy

Latency

Memory

Semantic Stability

Compare

InternVL3

VS

Qwen2.5VL

VS

Florence2

Generate

benchmark_vision.json
17. Material Dataset

Generate

dataset/

    assets/

    prompts/

    embeddings/

    fingerprints/

    statistics/

    labels/

Future

Training

Fine-tuning

LoRA

Recommendation

QA

18. Development Priority
Phase A

Replace Rule Semantic

↓

InternVL3

Phase B

Prompt Builder

Ground Truth Builder

Fingerprint

Phase C

Similarity Graph

Knowledge Base

FAISS Index

Phase D

Statistics Center

Regression Test

Benchmark

19. Out of Scope

The following modules belong to Version 4.

Material QA
Drift Detection
Tile Detection
Canonical Texture Generator
Image Quilting
Pattern Detector
Seamless Texture Generation
20. Deliverables

Version 3 should produce:

MaterialAssets/

KnowledgeBase/

PromptDataset/

Statistics/

Embeddings/

Fingerprints/

GroundTruth/

Regression/

Benchmarks/