RRepoGEO

REPOGEO REPORT · LITE

THU-LYJ-Lab/T3Bench

Default branch main · commit 6367462c · scanned 5/30/2026, 7:08:03 PM

GitHub: 1,100 stars · 11 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface THU-LYJ-Lab/T3Bench, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a clear license statement to the README

    Why:

    COPY-PASTE FIX
    This project is released under the [Your Chosen License Name] license. Please see the `LICENSE` file for full details.
  • highreadme#2
    Strengthen README's opening to clarify benchmark purpose

    Why:

    CURRENT
    T3Bench is the first comprehensive text-to-3D benchmark containing diverse text prompts of three increasing complexity levels that are specially designed for 3D generation (300 prompts in total).
    COPY-PASTE FIX
    For researchers and developers working on Text-to-3D generation, T3Bench provides the definitive framework for objective model evaluation. It is the first comprehensive text-to-3D benchmark containing diverse text prompts of three increasing complexity levels that are specially designed for 3D generation (300 prompts in total).
  • mediumtopics#3
    Add specific benchmark and evaluation topics

    Why:

    CURRENT
    3d, diffusion, nerf, text-to-3d
    COPY-PASTE FIX
    3d, diffusion, nerf, text-to-3d, benchmark, evaluation, text-to-3d-evaluation

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface THU-LYJ-Lab/T3Bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Amazon Mechanical Turk
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon Mechanical Turk · recommended 2×
  2. Three.js · recommended 2×
  3. PyTorch3D · recommended 2×
  4. Open3D · recommended 2×
  5. Blender · recommended 2×
  • CATEGORY QUERY
    How to objectively evaluate the quality and text alignment of generated 3D models?
    you: not recommended
    AI recommended (in order):
    1. open_clip
    2. Hugging Face's transformers
    3. Amazon Mechanical Turk
    4. Scale AI
    5. Appen
    6. Three.js
    7. Babylon.js
    8. PyTorch3D
    9. Open3D
    10. lpips
    11. Unity
    12. Unreal Engine
    13. Isaac Sim
    14. PyBullet
    15. Blender Cycles
    16. V-Ray
    17. Arnold
    18. Blender
    19. MeshLab
    20. 3D Viewer

    AI recommended 20 alternatives but never named THU-LYJ-Lab/T3Bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best benchmarks for comparing different text-to-3D generation techniques?
    you: not recommended
    AI recommended (in order):
    1. Amazon Mechanical Turk
    2. OpenAI CLIP
    3. `clip` Python library
    4. PyTorch3D
    5. Blender
    6. Three.js
    7. `pytorch-fid` library
    8. ShapeNet
    9. Objaverse
    10. Open3D
    11. `trimesh`
    12. Objaverse-XL
    13. `webdataset`
    14. DreamFusion
    15. Magic3D

    AI recommended 15 alternatives but never named THU-LYJ-Lab/T3Bench. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of THU-LYJ-Lab/T3Bench?
    pass
    AI named THU-LYJ-Lab/T3Bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts THU-LYJ-Lab/T3Bench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named THU-LYJ-Lab/T3Bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo THU-LYJ-Lab/T3Bench solve, and who is the primary audience?
    pass
    AI named THU-LYJ-Lab/T3Bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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THU-LYJ-Lab/T3Bench — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite