RRepoGEO

REPOGEO REPORT · LITE

apple/ml-hypersim

Default branch main · commit c85b2879 · scanned 5/18/2026, 8:38:22 AM

GitHub: 1,999 stars · 149 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
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 apple/ml-hypersim, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Add a concise, AI-friendly summary to the README's opening

    Why:

    CURRENT
    # The Hypersim Dataset

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding.
    COPY-PASTE FIX
    # The Hypersim Dataset

**Hypersim is a large-scale, photorealistic synthetic dataset designed for holistic indoor scene understanding, providing detailed per-pixel ground truth labels for computer vision research.**

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding.
  • lowhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL (e.g., `https://hypersim.github.io/`) to the repository's 'About' section.

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 apple/ml-hypersim
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Matterport3D
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Matterport3D · recommended 2×
  2. ScanNet · recommended 2×
  3. SUN RGB-D · recommended 2×
  4. Replica Dataset · recommended 1×
  5. Habitat-Matterport3D (HM3D) · recommended 1×
  • CATEGORY QUERY
    Where can I find photorealistic synthetic datasets for training indoor scene understanding models?
    you: not recommended
    AI recommended (in order):
    1. Matterport3D
    2. Replica Dataset
    3. ScanNet
    4. Habitat-Matterport3D (HM3D)
    5. Gibson Dataset
    6. SUN RGB-D
    7. AI2-THOR

    AI recommended 7 alternatives but never named apple/ml-hypersim. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which datasets provide detailed per-pixel ground truth labels for indoor computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. NYU Depth V2
    2. SUN RGB-D
    3. ScanNet
    4. Matterport3D
    5. ADE20K
    6. Stanford 2D-3D-S

    AI recommended 6 alternatives but never named apple/ml-hypersim. 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 apple/ml-hypersim?
    pass
    AI named apple/ml-hypersim explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of apple/ml-hypersim. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/apple/ml-hypersim"><img src="https://repogeo.com/badge/apple/ml-hypersim.svg" alt="RepoGEO" /></a>
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apple/ml-hypersim — 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