RRepoGEO

REPOGEO REPORT · LITE

ActiveVisionLab/Awesome-LLM-3D

Default branch avl-branch · commit f01b39ba · scanned 5/11/2026, 10:07:57 AM

GitHub: 2,195 stars · 140 forks

AI VISIBILITY SCORE
22 /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
1 / 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 ActiveVisionLab/Awesome-LLM-3D, 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
  • hightopics#1
    Add descriptive topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, llm, 3d, multimodal, survey, papers, resources, computer-vision, deep-learning
  • highreadme#2
    Reinforce 'awesome list' and 'survey' nature in README's opening

    Why:

    CURRENT
    ## 🏠 About
Here is a curated list of papers about 3D-Related Tasks empowered by Large Language Models (LLMs).
    COPY-PASTE FIX
    ## 🏠 About
This repository, Awesome-LLM-3D, is a comprehensive and actively curated list of papers and resources focusing on 3D-Related Tasks empowered by Multi-modal Large Language Models (LLMs). It also serves as the official companion for our survey paper.
  • mediumhomepage#3
    Add a homepage link to the associated survey paper's project page

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2405.10255v2

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 ActiveVisionLab/Awesome-LLM-3D
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DreamFusion
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DreamFusion · recommended 2×
  2. Magic3D · recommended 2×
  3. GET3D · recommended 2×
  4. Point-E · recommended 2×
  5. arXiv.org · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey of large language models applied to 3D tasks?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. DreamFusion
    3. Magic3D
    4. GET3D
    5. Point-E
    6. Shap-E
    7. Papers With Code
    8. Google Scholar
    9. OpenReview.net
    10. NeurIPS
    11. ICLR
    12. CVPR
    13. ICCV
    14. YouTube
    15. Two Minute Papers
    16. Lex Fridman Podcast
    17. SIGGRAPH
    18. Towards Data Science
    19. Medium

    AI recommended 19 alternatives but never named ActiveVisionLab/Awesome-LLM-3D. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements in using multimodal LLMs for 3D scene understanding and generation?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Fantasia3D
    4. GET3D
    5. GPT-4V
    6. OpenScene
    7. SceneVerse
    8. PaLM-E
    9. RT-2
    10. Point-E
    11. LGM

    AI recommended 11 alternatives but never named ActiveVisionLab/Awesome-LLM-3D. 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 ActiveVisionLab/Awesome-LLM-3D?
    pass
    AI did not name ActiveVisionLab/Awesome-LLM-3D — likely talking about a different project

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

  • If a team adopts ActiveVisionLab/Awesome-LLM-3D in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ActiveVisionLab/Awesome-LLM-3D 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 ActiveVisionLab/Awesome-LLM-3D solve, and who is the primary audience?
    pass
    AI did not name ActiveVisionLab/Awesome-LLM-3D — likely talking about a different project

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

Embed your GEO score

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ActiveVisionLab/Awesome-LLM-3D — 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