RRepoGEO

REPOGEO REPORT · LITE

nianticlabs/acezero

Default branch main · commit 450bfe2a · scanned 6/8/2026, 2:03:05 AM

GitHub: 805 stars · 54 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 nianticlabs/acezero, 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 clearly state its purpose as a learning-based SfM framework

    Why:

    CURRENT
    # ACE0 (ACE Zero)

This repository contains the code associated to the ACE0 paper:
    COPY-PASTE FIX
    # ACE0 (ACE Zero): Learning-based Structure-from-Motion for Camera Pose Estimation and 3D Scene Reconstruction

ACE0 is a cutting-edge, learning-based structure-from-motion (SfM) approach that estimates camera parameters of sets of images by learning a multi-view consistent, implicit scene representation. This repository provides the official code for the ECCV 2024 oral paper:
  • mediumtopics#2
    Add more specific topics to highlight learning-based and implicit representation aspects

    Why:

    CURRENT
    3d-reconstruction, camera-relocalization, computer-vision, eccv, eccv2024, machine-learning, pose-estimation, sfm, structure-from-motion, visual-relocalization
    COPY-PASTE FIX
    3d-reconstruction, camera-relocalization, computer-vision, eccv, eccv2024, machine-learning, pose-estimation, sfm, structure-from-motion, visual-relocalization, learning-based-sfm, implicit-scene-representation, neural-rendering-alternative, multi-view-geometry
  • lowlicense#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is released under [describe the actual license, e.g., 'a custom license based on X and Y', or 'the terms specified in the LICENSE file']. Please refer to the [LICENSE](LICENSE) file for full details.

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 nianticlabs/acezero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
COLMAP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. COLMAP · recommended 1×
  2. OpenMVG · recommended 1×
  3. OpenMVS · recommended 1×
  4. Metashape · recommended 1×
  5. RealityCapture · recommended 1×
  • CATEGORY QUERY
    How to accurately estimate camera poses and reconstruct scenes from multiple images?
    you: not recommended
    AI recommended (in order):
    1. COLMAP
    2. OpenMVG
    3. OpenMVS
    4. Metashape
    5. RealityCapture
    6. Meshroom
    7. AliceVision
    8. VisualSFM
    9. CMVS
    10. PMVS

    AI recommended 10 alternatives but never named nianticlabs/acezero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best learning-based methods for robust 3D scene reconstruction from image sequences?
    you: not recommended
    AI recommended (in order):
    1. Neural Radiance Fields (NeRF)
    2. Instant-NGP
    3. Mip-NeRF 360
    4. K-Planes
    5. Neuralangelo
    6. DeepSDF
    7. NeuralRecon
    8. Point-NeRF
    9. VolSDF
    10. MonoSDF

    AI recommended 10 alternatives but never named nianticlabs/acezero. 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 nianticlabs/acezero?
    pass
    AI did not name nianticlabs/acezero — 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?

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

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

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nianticlabs/acezero — 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