RRepoGEO

REPOGEO REPORT · LITE

aharley/simple_bev

Default branch main · commit be46f0ef · scanned 6/4/2026, 5:52:56 AM

GitHub: 635 stars · 92 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 aharley/simple_bev, 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
  • hightopics#1
    Add relevant topics for discoverability

    Why:

    COPY-PASTE FIX
    ["bev-perception", "multi-sensor", "autonomous-driving", "computer-vision", "deep-learning", "pytorch", "baseline", "robotics"]
  • highreadme#2
    Reposition README introduction to highlight core value and audience

    Why:

    CURRENT
    # Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

This is the official code release for our arXiv paper on BEV perception.
    COPY-PASTE FIX
    # Simple-BEV: A Simple Baseline for Multi-Sensor BEV Perception

Simple-BEV provides a clear, self-contained implementation for Bird's Eye View (BEV) perception, focusing on what truly matters for multi-sensor fusion (camera and radar). It serves as a robust, readable baseline for researchers and developers in autonomous driving, robotics, and computer vision, prioritizing simplicity and educational value over complex state-of-the-art architectures.
  • mediumreadme#3
    Emphasize the 'not production-ready' disclaimer in the README

    Why:

    COPY-PASTE FIX
    ## Important Note

**This project is *not* a production-ready solution, but rather a starting point for understanding and experimenting with BEV projection.** It is intended for research and educational purposes.

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 aharley/simple_bev
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenPCDet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenPCDet · recommended 2×
  2. BEVFormer · recommended 1×
  3. BEVDepth · recommended 1×
  4. LSS (Lift, Splat, Shoot) · recommended 1×
  5. CenterPoint · recommended 1×
  • CATEGORY QUERY
    Seeking a robust baseline for multi-sensor Bird's Eye View perception in autonomous vehicles.
    you: not recommended
    AI recommended (in order):
    1. BEVFormer
    2. BEVDepth
    3. LSS (Lift, Splat, Shoot)
    4. OpenPCDet
    5. CenterPoint
    6. DETR3D

    AI recommended 6 alternatives but never named aharley/simple_bev. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement Bird's Eye View perception fusing camera and radar data?
    you: not recommended
    AI recommended (in order):
    1. nuScenes-devkit
    2. OpenPCDet
    3. MMDetection3D
    4. ROS (Robot Operating System)
    5. PyTorch
    6. TensorFlow
    7. OpenCV

    AI recommended 7 alternatives but never named aharley/simple_bev. 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 aharley/simple_bev?
    pass
    AI did not name aharley/simple_bev — 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 aharley/simple_bev in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name aharley/simple_bev — 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?

  • In one sentence, what problem does the repo aharley/simple_bev solve, and who is the primary audience?
    pass
    AI named aharley/simple_bev explicitly

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

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aharley/simple_bev — 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