RRepoGEO

REPOGEO REPORT · LITE

HuaiyuanXu/3D-Occupancy-Perception

Default branch main · commit 85edcc14 · scanned 5/31/2026, 7:42:46 PM

GitHub: 610 stars · 36 forks

AI VISIBILITY SCORE
22 /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
1 / 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 HuaiyuanXu/3D-Occupancy-Perception, 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 the README H1 to clearly state it's a survey

    Why:

    CURRENT
    # We research 3D Occupancy Perception for Autonomous Driving
    COPY-PASTE FIX
    # A Comprehensive Survey on 3D Occupancy Perception for Autonomous Driving
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root directory to clarify usage rights.
  • mediumtopics#3
    Expand topics to include 'survey' and 'review'

    Why:

    CURRENT
    autonomous-driving, occupancy-perception, occupancy-survey
    COPY-PASTE FIX
    autonomous-driving, occupancy-perception, occupancy-survey, survey, review, literature-review

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 HuaiyuanXu/3D-Occupancy-Perception
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BEVFormer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BEVFormer · recommended 2×
  2. PointPillars · recommended 2×
  3. SECOND · recommended 2×
  4. VoxelNet · recommended 2×
  5. PETR · recommended 1×
  • CATEGORY QUERY
    What are the best techniques for 3D dense perception in autonomous driving systems?
    you: not recommended
    AI recommended (in order):
    1. BEVFormer
    2. PETR
    3. UniAD
    4. PointPillars
    5. SECOND
    6. VoxelNet
    7. F-PointNet
    8. MVX-Net
    9. PointPainting
    10. MonoDETR
    11. DD3D
    12. M3D-RPN
    13. OccNet
    14. SurroundOcc
    15. StreetGaussians
    16. Urban Radiance Fields

    AI recommended 16 alternatives but never named HuaiyuanXu/3D-Occupancy-Perception. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a comprehensive overview of information fusion methods in autonomous driving perception.
    you: not recommended
    AI recommended (in order):
    1. Kalman Filters
    2. Extended Kalman Filters
    3. Unscented Kalman Filters
    4. Particle Filters
    5. Occupancy Grid Maps
    6. PointPillars
    7. Frustum PointNets
    8. VoxelNet
    9. SECOND
    10. CenterPoint
    11. Graph Neural Networks
    12. Hungarian Algorithm
    13. Joint Probabilistic Data Association Filter
    14. Federated Kalman Filters
    15. BEVFormer
    16. TransFuser

    AI recommended 16 alternatives but never named HuaiyuanXu/3D-Occupancy-Perception. 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 HuaiyuanXu/3D-Occupancy-Perception?
    pass
    AI did not name HuaiyuanXu/3D-Occupancy-Perception — 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 HuaiyuanXu/3D-Occupancy-Perception in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HuaiyuanXu/3D-Occupancy-Perception 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 HuaiyuanXu/3D-Occupancy-Perception solve, and who is the primary audience?
    pass
    AI did not name HuaiyuanXu/3D-Occupancy-Perception — 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?

Embed your GEO score

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MARKDOWN (README)
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HuaiyuanXu/3D-Occupancy-Perception — 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