RRepoGEO

REPOGEO REPORT · LITE

TQTQliu/MVSGaussian

Default branch main · commit 86ee0df0 · scanned 6/1/2026, 4:52:56 AM

GitHub: 567 stars · 37 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 TQTQliu/MVSGaussian, 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 concise value proposition to the README's opening

    Why:

    CURRENT
    The README currently starts with an H2 title followed by author and affiliation lists.
    COPY-PASTE FIX
    Add a paragraph immediately after the main title (H2) and before the author list, such as: "MVSGaussian introduces a novel approach for **fast and generalizable 3D scene reconstruction and novel view synthesis** from multi-view stereo images. Unlike traditional methods that train from scratch, MVSGaussian leverages **pre-trained Gaussian Splatting models** to achieve high-quality results with significantly improved efficiency and generalization capabilities."
  • mediumreadme#2
    Add a 'Why MVSGaussian?' or 'Comparison' section

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., "## Why MVSGaussian?" or "## Comparison", that highlights its unique benefits (e.g., speed, generalizability, pre-trained models) compared to common alternatives like scene-specific Gaussian Splatting methods or NeRF-based approaches.
  • lowtopics#3
    Add broader, relevant topics

    Why:

    CURRENT
    feed-forward-gaussian-splatting, gaussian-splatting, generalizable, multi-view-stereo, novel-view-synthesis
    COPY-PASTE FIX
    Add the following topics: 3d-reconstruction, computer-vision, deep-learning, neural-rendering.

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 TQTQliu/MVSGaussian
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
3D Gaussian Splatting (3DGS)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. 3D Gaussian Splatting (3DGS) · recommended 1×
  2. Mip-Splatting · recommended 1×
  3. Zip-NeRF360 · recommended 1×
  4. Splatfacto · recommended 1×
  5. GaussianPro · recommended 1×
  • CATEGORY QUERY
    What are fast and generalizable methods for 3D scene reconstruction using Gaussian splatting?
    you: not recommended
    AI recommended (in order):
    1. 3D Gaussian Splatting (3DGS)
    2. Mip-Splatting
    3. Zip-NeRF360
    4. Splatfacto
    5. GaussianPro
    6. Luma AI's "Radiance Fields"

    AI recommended 6 alternatives but never named TQTQliu/MVSGaussian. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to achieve efficient novel view synthesis from multiple camera images for 3D scenes?
    you: not recommended
    AI recommended (in order):
    1. Instant-NGP
    2. Plenoxels
    3. 3D Gaussian Splatting
    4. K-Planes
    5. Mip-NeRF 360
    6. Neural Radiance Fields (NeRF)
    7. NeRF++

    AI recommended 7 alternatives but never named TQTQliu/MVSGaussian. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 TQTQliu/MVSGaussian?
    pass
    AI named TQTQliu/MVSGaussian explicitly

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

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

    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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MARKDOWN (README)
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HTML
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TQTQliu/MVSGaussian — 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