RRepoGEO

REPOGEO REPORT · LITE

Fyusion/LLFF

Default branch master · commit c6e27b1e · scanned 5/18/2026, 2:04:12 PM

GitHub: 1,697 stars · 251 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's opening to highlight problem-solving

    Why:

    CURRENT
    # Local Light Field Fusion
### Project | Video | Paper 

Tensorflow implementation for novel view synthesis from sparse input images.
    COPY-PASTE FIX
    # Local Light Field Fusion: High-Quality Novel View Synthesis from Sparse Images
### Project | Video | Paper 

This repository provides the Tensorflow implementation for Local Light Field Fusion, a method designed to synthesize high-quality novel views from a significantly sparser set of input images than traditional methods.
  • mediumtopics#2
    Add specific topics for novel view synthesis and 3D reconstruction

    Why:

    CURRENT
    deep-learning, light-field, rendering, view-synthesis
    COPY-PASTE FIX
    deep-learning, light-field, rendering, view-synthesis, novel-view-synthesis, 3d-reconstruction
  • lowabout#3
    Update repository description to be problem-solution oriented

    Why:

    CURRENT
    Code release for Local Light Field Fusion at SIGGRAPH 2019
    COPY-PASTE FIX
    TensorFlow implementation for high-quality novel view synthesis from sparse input images using Local Light Field Fusion (SIGGRAPH 2019).

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NeRF · recommended 1×
  2. NVlabs/instant-ngp · recommended 1×
  3. jonbarron/mipnerf360 · recommended 1×
  4. sarafridov/K-Planes · recommended 1×
  5. colmap/colmap · recommended 1×
  • CATEGORY QUERY
    How can I generate new camera perspectives from a sparse set of existing images?
    you: not recommended
    AI recommended (in order):
    1. NeRF
    2. Instant-NGP (NVlabs/instant-ngp)
    3. Mip-NeRF 360 (jonbarron/mipnerf360)
    4. K-Planes (sarafridov/K-Planes)
    5. COLMAP (colmap/colmap)
    6. OpenMVS (cdcseacave/openMVS)
    7. 3D Gaussian Splatting (graphdeco-inria/gaussian-splatting)
    8. Metashape
    9. Meshroom (alicevision/meshroom)

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

    Show full AI answer
  • CATEGORY QUERY
    What deep learning library helps with novel view synthesis for 3D scene reconstruction?
    you: not recommended
    AI recommended (in order):
    1. PyTorch3D (facebookresearch/pytorch3d)
    2. TensorFlow Graphics (tensorflow/graphics)
    3. Keras (keras-team/keras)
    4. Open3D (isl-org/Open3D)
    5. MONAI (Project-MONAI/MONAI)

    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