RRepoGEO

REPOGEO REPORT · LITE

ActiveVisionLab/Awesome-LLM-3D

Default branch avl-branch · commit f01b39ba · scanned 6/21/2026, 2:32:55 PM

GitHub: 2,224 stars · 142 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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
  • highreadme#1
    Reposition README H1 to clearly state it's an 'Awesome List'

    Why:

    CURRENT
    # Awesome-LLM-3D
    COPY-PASTE FIX
    # Awesome-LLM-3D: A Curated List of Multi-modal LLM Resources for 3D World Tasks
  • hightopics#2
    Add relevant GitHub topics

    Why:

    COPY-PASTE FIX
    awesome-list, llm, 3d, multi-modal-llm, computer-vision, natural-language-processing, deep-learning, research-papers, survey, embodied-ai, 3d-understanding, 3d-generation
  • mediumhomepage#3
    Add a homepage URL

    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
Habitat
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Habitat · recommended 2×
  2. OpenScene · recommended 1×
  3. CLIP · recommended 1×
  4. PointCLIP · recommended 1×
  5. ULIP · recommended 1×
  • CATEGORY QUERY
    What are the best resources for applying large language models to 3D scene understanding?
    you: not recommended
    AI recommended (in order):
    1. OpenScene
    2. CLIP
    3. PointCLIP
    4. ULIP
    5. LISA
    6. GPT-4V
    7. Gemini 1.5 Pro
    8. Habitat-Matterport 3D (HM3D) Dataset
    9. ScanNet
    10. AI2-THOR
    11. RoboTHOR
    12. Habitat
    13. 3D-LLM

    AI recommended 13 alternatives but never named ActiveVisionLab/Awesome-LLM-3D. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can multi-modal LLMs be used for 3D generation, reasoning, and embodied AI?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Point-E
    4. GET3D
    5. Shap-E
    6. PaLM-E
    7. EmbodiedGPT
    8. SayCan
    9. RT-1
    10. VoxPoser
    11. ALOHA
    12. Habitat
    13. RoboCat

    AI recommended 13 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 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?

  • 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