RRepoGEO

REPOGEO REPORT · LITE

vasgaowei/BEV-Perception

Default branch main · commit e597b85a · scanned 5/30/2026, 8:48:02 AM

GitHub: 705 stars · 44 forks

AI VISIBILITY SCORE
28 /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
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 vasgaowei/BEV-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's opening sentence to clarify it's a curated list

    Why:

    CURRENT
    This is a repository for Bird's Eye View Perception, including 3D object detection, segmentation, online-mapping and occupancy prediction.
    COPY-PASTE FIX
    This repository curates an awesome list of papers and projects on Bird's Eye View Perception, covering 3D object detection, segmentation, online-mapping, and occupancy prediction.
  • mediumhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/vasgaowei/BEV-Perception
  • lowtopics#3
    Add 'awesome-list' to the repository topics

    Why:

    CURRENT
    autonomous-driving, autonomous-vehicles, bev-perception, hdmap, occupancy-prediction
    COPY-PASTE FIX
    autonomous-driving, autonomous-vehicles, bev-perception, hdmap, occupancy-prediction, awesome-list

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 vasgaowei/BEV-Perception
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ROS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ROS · recommended 2×
  2. OpenPCDet · recommended 1×
  3. nuScenes Development Kit · recommended 1×
  4. MMDetection3D · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What are the best tools for bird's eye view perception in autonomous vehicles?
    you: not recommended
    AI recommended (in order):
    1. OpenPCDet
    2. nuScenes Development Kit
    3. MMDetection3D
    4. PyTorch
    5. TensorFlow
    6. ROS
    7. PCL
    8. Apollo

    AI recommended 8 alternatives but never named vasgaowei/BEV-Perception. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement 3D object detection and occupancy prediction for self-driving cars?
    you: not recommended
    AI recommended (in order):
    1. OpenPCDet (open-mmlab/OpenPCDet)
    2. MMDetection3D (open-mmlab/mmdetection3d)
    3. Apollo (ApolloAuto/apollo)
    4. ROS
    5. PCL (PointCloudLibrary/pcl)
    6. OctoMap (OctoMap/octomap)
    7. NVIDIA DriveWorks
    8. TensorFlow (tensorflow/tensorflow)
    9. PyTorch (pytorch/pytorch)

    AI recommended 9 alternatives but never named vasgaowei/BEV-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 vasgaowei/BEV-Perception?
    pass
    AI named vasgaowei/BEV-Perception explicitly

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

  • If a team adopts vasgaowei/BEV-Perception in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vasgaowei/BEV-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 vasgaowei/BEV-Perception solve, and who is the primary audience?
    pass
    AI did not name vasgaowei/BEV-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

Drop this badge into the README of vasgaowei/BEV-Perception. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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vasgaowei/BEV-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