RRepoGEO

REPOGEO REPORT · LITE

kerrj/lerf

Default branch main · commit db08d578 · scanned 6/12/2026, 10:03:01 PM

GitHub: 730 stars · 76 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 kerrj/lerf, 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
  • highreadme#1
    Enhance the README's introductory statement

    Why:

    CURRENT
    # LERF: Language Embedded Radiance Fields
This is the official implementation for LERF.
    COPY-PASTE FIX
    # LERF: Language Embedded Radiance Fields
This is the official implementation for LERF, a framework that enables semantic understanding and interaction with 3D NeRF scenes through natural language prompts. LERF allows users to query and edit specific regions of a reconstructed 3D scene using text, leveraging a CLIP-field for semantic embedding.
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

LERF's core differentiator is its integration of language-based semantic understanding and control directly into radiance fields (NeRFs). Unlike [Competitor A] or [Competitor B], LERF focuses on enabling users to specify and edit specific regions of a reconstructed 3D scene using natural language prompts, leveraging a 'CLIP-field' for semantic embedding. This allows for more intuitive and precise interaction with 3D environments based on textual descriptions.

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 kerrj/lerf
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP-NeRF
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP-NeRF · recommended 2×
  2. OpenAI CLIP · recommended 1×
  3. DreamFusion · recommended 1×
  4. Point-E · recommended 1×
  5. Shap-E · recommended 1×
  • CATEGORY QUERY
    How can I query 3D environments and objects using natural language text prompts?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. CLIP-NeRF
    3. DreamFusion
    4. Point-E
    5. Shap-E
    6. Google ScaNeRF
    7. StreetFusion
    8. NVIDIA Instant NeRF
    9. NVIDIA Kaolin Wisp
    10. Unity
    11. Unreal Engine
    12. GPT-3.5
    13. GPT-4
    14. Hugging Face Transformers library
    15. BERT
    16. RoBERTa
    17. PostgreSQL
    18. Neo4j
    19. Matterport3D Dataset
    20. ScanNet Dataset

    AI recommended 20 alternatives but never named kerrj/lerf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks allow embedding semantic understanding into neural radiance fields for search?
    you: not recommended
    AI recommended (in order):
    1. LERF (Language Embedded Radiance Fields)
    2. CLIP-NeRF
    3. Semantic-NeRF
    4. NeRF-W (NeRF in the Wild)
    5. K-Planes
    6. Instant-NGP

    AI recommended 6 alternatives but never named kerrj/lerf. 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 kerrj/lerf?
    pass
    AI named kerrj/lerf explicitly

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

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

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

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kerrj/lerf — 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