RRepoGEO

REPOGEO REPORT · LITE

Fyusion/LLFF

Default branch master · commit c6e27b1e · scanned 6/29/2026, 8:22:47 PM

GitHub: 1,701 stars · 252 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
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 Fyusion/LLFF, 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 README opening to highlight solution and audience

    Why:

    CURRENT
    Tensorflow implementation for novel view synthesis from sparse input images.
    COPY-PASTE FIX
    Fyusion/LLFF provides a robust Tensorflow implementation for **practical novel view synthesis from sparse input images**, enabling users to generate high-quality, continuous 5D light field representations. This project, presented at SIGGRAPH 2019, offers a powerful solution for researchers and practitioners in 3D reconstruction and computer graphics.
  • mediumtopics#2
    Add more specific topics for better query matching

    Why:

    CURRENT
    deep-learning, light-field, rendering, view-synthesis
    COPY-PASTE FIX
    deep-learning, light-field, rendering, view-synthesis, novel-view-synthesis, 3d-reconstruction, computer-vision, neural-rendering
  • mediumreadme#3
    Add a 'Key Features' or 'How it Works' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features

LLFF differentiates itself by representing a scene as a **collection of localized neural light fields**, learned from sparse input images. These localized fields are then fused to synthesize novel views, offering a practical approach to view synthesis with prescriptive sampling guidelines.

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 Fyusion/LLFF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NeRF
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NeRF · recommended 2×
  2. colmap/colmap · recommended 1×
  3. MVS-Texturing · recommended 1×
  4. cdcseacave/openMVS · recommended 1×
  5. NVlabs/instant-ngp · recommended 1×
  • CATEGORY QUERY
    How to generate new views from a limited set of input images?
    you: not recommended
    AI recommended (in order):
    1. COLMAP (colmap/colmap)
    2. MVS-Texturing
    3. OpenMVS (cdcseacave/openMVS)
    4. Instant NGP (NVlabs/instant-ngp)
    5. NeRF
    6. nerfstudio (nerfstudio-project/nerfstudio)
    7. Meshroom (alicevision/meshroom)
    8. Metashape
    9. 3D Gaussian Splatting

    AI recommended 9 alternatives but never named Fyusion/LLFF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a deep learning solution for novel view synthesis using sparse image inputs.
    you: not recommended
    AI recommended (in order):
    1. NeRF
    2. Instant-NGP
    3. MVSNeRF
    4. PixelNeRF
    5. IBRNet

    AI recommended 5 alternatives but never named Fyusion/LLFF. 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 Fyusion/LLFF?
    pass
    AI named Fyusion/LLFF explicitly

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

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

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

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Fyusion/LLFF — 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