RRepoGEO

REPOGEO REPORT · LITE

Charmve/OccNet-Course

Default branch main · commit 5a4b5dec · scanned 6/2/2026, 1:53:15 AM

GitHub: 771 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 Charmve/OccNet-Course, 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
    Add a clear, concise opening statement to the README

    Why:

    CURRENT
    The README currently starts with '2024 各家bev-occ方案进展' and then 'News!'.
    COPY-PASTE FIX
    Add the following as the very first line of your README:
# OccNet-Course: 国内首个占据栅格网络全栈课程《从BEV到Occupancy Network，算法原理与工程实践》，包含端侧部署。

This repository provides a comprehensive Surrounding Semantic Occupancy Perception Course for Autonomous Driving, including documentation, presentations, and source code.
  • highhomepage#2
    Add the course homepage URL to the repository's 'Homepage' field

    Why:

    COPY-PASTE FIX
    http://111.229.117.200:8100/
  • mediumreadme#3
    Add a dedicated 'About This Course' section to the README

    Why:

    CURRENT
    The README currently focuses on news and updates initially, without a dedicated section explaining the course's content and benefits.
    COPY-PASTE FIX
    Add a new section early in the README, for example:

## About This Course

This course is the first comprehensive full-stack curriculum on Occupancy Networks in China, covering everything from BEV (Bird's-Eye View) to Occupancy Network principles and engineering practices, including edge-side deployment. It is designed for students and professionals interested in 3D computer vision and robotics for autonomous driving, offering both theoretical foundations and practical implementation 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 Charmve/OccNet-Course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Udacity's Self-Driving Car Engineer Nanodegree
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Udacity's Self-Driving Car Engineer Nanodegree · recommended 1×
  2. Coursera's "Autonomous Driving" Specialization by the University of Toronto · recommended 1×
  3. Stanford University's CS231A: Computer Vision, from 3D Reconstruction to Recognition · recommended 1×
  4. NVIDIA's Deep Learning Institute (DLI) Workshops · recommended 1×
  5. University of Michigan's "Self-Driving Cars" Specialization (Coursera) · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive course on occupancy networks for self-driving cars?
    you: not recommended
    AI recommended (in order):
    1. Udacity's Self-Driving Car Engineer Nanodegree
    2. Coursera's "Autonomous Driving" Specialization by the University of Toronto
    3. Stanford University's CS231A: Computer Vision, from 3D Reconstruction to Recognition
    4. NVIDIA's Deep Learning Institute (DLI) Workshops
    5. University of Michigan's "Self-Driving Cars" Specialization (Coursera)
    6. Robotics Academy

    AI recommended 6 alternatives but never named Charmve/OccNet-Course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement end-to-end BEV occupancy prediction in autonomous vehicle systems?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. TensorFlow Lite
    4. OpenCV
    5. NumPy
    6. ROS / ROS 2
    7. Open3D
    8. ONNX Runtime

    AI recommended 8 alternatives but never named Charmve/OccNet-Course. 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 Charmve/OccNet-Course?
    pass
    AI did not name Charmve/OccNet-Course — 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 Charmve/OccNet-Course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Charmve/OccNet-Course 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 Charmve/OccNet-Course solve, and who is the primary audience?
    pass
    AI did not name Charmve/OccNet-Course — 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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Charmve/OccNet-Course — 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