RRepoGEO

REPOGEO REPORT · LITE

fabiotosi92/Awesome-Deep-Stereo-Matching

Default branch main · commit e524ed05 · scanned 6/12/2026, 2:57:57 AM

GitHub: 587 stars · 29 forks

AI VISIBILITY SCORE
33 /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
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 fabiotosi92/Awesome-Deep-Stereo-Matching, 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 the README's opening to emphasize its role as a definitive index and discovery hub

    Why:

    CURRENT
    Welcome to the "Awesome-Deep-Stereo-Matching" repository, a curated list of state-of-the-art deep stereo matching resources maintained by Fabio Tosi, Matteo Poggi and Luca Bartolomei, from the University of Bologna. This repository, inspired by awesome-computer-vision, aims to provide a comprehensive collection of the latest and most influential papers on deep stereo matching published in top-tier computer vision conferences and prestigious journals.
    COPY-PASTE FIX
    Welcome to the "Awesome-Deep-Stereo-Matching" repository, the definitive curated list and index of state-of-the-art deep stereo matching resources. Maintained by Fabio Tosi, Matteo Poggi and Luca Bartolomei from the University of Bologna, this awesome list provides a comprehensive, categorized collection of the latest and most influential papers, code, and datasets on deep stereo matching published in top-tier computer vision conferences and prestigious journals. It serves as a central hub for researchers and practitioners to discover and navigate the field.
  • mediumtopics#2
    Add 'awesome-list' and 'curated-list' to repository topics

    Why:

    CURRENT
    deep-stereo, deep-stereo-network, depth-estimation, stereo, stereo-algorithms, stereo-camera, stereo-depth-estimation, stereo-matching, stereo-vision
    COPY-PASTE FIX
    deep-stereo, deep-stereo-network, depth-estimation, stereo, stereo-algorithms, stereo-camera, stereo-depth-estimation, stereo-matching, stereo-vision, awesome-list, curated-list, research-papers, computer-vision-resources
  • lowcomparison#3
    Add a small section to the README differentiating this list from other resource types

    Why:

    COPY-PASTE FIX
    Add a small section to the README, perhaps titled 'Why this list?' or 'How is this different?', explaining that unlike active benchmarks (e.g., Middlebury, KITTI) or general search engines (e.g., Google Scholar, Papers With Code), this repository provides a human-curated, categorized, and opinionated selection of the most influential works, specifically designed for focused research and quick discovery within Deep Stereo Matching.

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 fabiotosi92/Awesome-Deep-Stereo-Matching
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code - Stereo Matching
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code - Stereo Matching · recommended 1×
  2. Middlebury Stereo Vision Page · recommended 1×
  3. KITTI Vision Benchmark Suite - Stereo · recommended 1×
  4. ETH3D Stereo Benchmarks · recommended 1×
  5. Google Scholar · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of state-of-the-art deep stereo matching techniques?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code - Stereo Matching
    2. Middlebury Stereo Vision Page
    3. KITTI Vision Benchmark Suite - Stereo
    4. ETH3D Stereo Benchmarks
    5. Google Scholar
    6. arXiv

    AI recommended 6 alternatives but never named fabiotosi92/Awesome-Deep-Stereo-Matching. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most influential papers and methods for deep learning-based stereo depth estimation?
    you: not recommended
    AI recommended (in order):
    1. DispNet
    2. FlyingThings3D
    3. GC-Net
    4. PSMNet
    5. AnyNet
    6. RAFT-Stereo
    7. GMA-Stereo
    8. CFNet

    AI recommended 8 alternatives but never named fabiotosi92/Awesome-Deep-Stereo-Matching. 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 fabiotosi92/Awesome-Deep-Stereo-Matching?
    pass
    AI named fabiotosi92/Awesome-Deep-Stereo-Matching explicitly

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

  • If a team adopts fabiotosi92/Awesome-Deep-Stereo-Matching in production, what risks or prerequisites should they evaluate first?
    pass
    AI named fabiotosi92/Awesome-Deep-Stereo-Matching 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 fabiotosi92/Awesome-Deep-Stereo-Matching solve, and who is the primary audience?
    pass
    AI did not name fabiotosi92/Awesome-Deep-Stereo-Matching — 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?

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fabiotosi92/Awesome-Deep-Stereo-Matching — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite