RRepoGEO

REPOGEO REPORT · LITE

Pang-Yatian/Point-MAE

Default branch main · commit 7445a680 · scanned 6/8/2026, 2:06:49 AM

GitHub: 630 stars · 73 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 Pang-Yatian/Point-MAE, 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
    Clarify project name in README H1 to match AI's competitor recognition

    Why:

    CURRENT
    # Point-MAE
    COPY-PASTE FIX
    # Point-MAE (PointMAE)
  • mediumhomepage#2
    Add the project's academic paper link as the homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2203.06604
  • lowtopics#3
    Expand topics to include core methodology and broader domain

    Why:

    CURRENT
    point-cloud, self-supervised-learning
    COPY-PASTE FIX
    point-cloud, self-supervised-learning, masked-autoencoders, autoencoders, 3d-vision

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 Pang-Yatian/Point-MAE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PointMAE
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PointMAE · recommended 2×
  2. Point-BERT · recommended 2×
  3. PointCL · recommended 2×
  4. GraphMAE · recommended 2×
  5. PointInfoNCE · recommended 1×
  • CATEGORY QUERY
    How can I pre-train models for 3D point cloud classification using self-supervision?
    you: not recommended
    AI recommended (in order):
    1. PointMAE
    2. Point-BERT
    3. PointCL
    4. PointInfoNCE
    5. PointNet++
    6. DGCNN
    7. GraphMAE

    AI recommended 7 alternatives but never named Pang-Yatian/Point-MAE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective self-supervised learning methods for robust point cloud feature extraction?
    you: not recommended
    AI recommended (in order):
    1. PointMAE
    2. Point-BERT
    3. PointCL
    4. Self-PointFlow
    5. PointContrast
    6. GraphMAE

    AI recommended 6 alternatives but never named Pang-Yatian/Point-MAE. 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 Pang-Yatian/Point-MAE?
    pass
    AI named Pang-Yatian/Point-MAE explicitly

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

  • If a team adopts Pang-Yatian/Point-MAE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Pang-Yatian/Point-MAE 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 Pang-Yatian/Point-MAE solve, and who is the primary audience?
    pass
    AI named Pang-Yatian/Point-MAE explicitly

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

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MARKDOWN (README)
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Pang-Yatian/Point-MAE — RepoGEO report