REPOGEO REPORT · LITE

zhulf0804/3D-PointCloud

Default branch master · commit 6d17d1df · scanned 6/27/2026, 8:02:51 AM

GitHub: 2,922 stars · 327 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 zhulf0804/3D-PointCloud, 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 H1 and opening sentence to emphasize "curated list"

Why:

CURRENT

# 3D - Point Cloud

**Paper list** and **Datasets** about Point Cloud.

COPY-PASTE FIX

# Awesome 3D Point Cloud: Curated Papers and Datasets

This repository is an **awesome and curated collection** of essential research papers and datasets related to 3D Point Clouds, focusing on areas like autonomous driving, detection, and segmentation.

highlicense#2

Add a LICENSE file and declare its type

Why:

COPY-PASTE FIX

(Create a `LICENSE` file in the root of the repository with your chosen open-source license, e.g., MIT. Then, add a line to your README, for example: `## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.`)

mediumhomepage#3

Add a homepage URL to the repository settings

Why:

COPY-PASTE FIX

https://github.com/zhulf0804/3D-PointCloud (Add this URL in the "About" section of the GitHub repository settings.)

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 zhulf0804/3D-PointCloud

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

KITTI Vision Benchmark Suite

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

KITTI Vision Benchmark Suite · recommended 2×
Awesome Point Cloud Analysis · recommended 1×
Papers With Code · recommended 1×
Semantic3D.net · recommended 1×
Stanford 3D Indoor Semantic Parsing and Reconstruction Dataset (S3DIS) · recommended 1×

CATEGORY QUERY
Where can I find a curated list of research papers and datasets on 3D point clouds?
you: not recommended
AI recommended (in order):
1. Awesome Point Cloud Analysis
2. Papers With Code
3. KITTI Vision Benchmark Suite
4. Semantic3D.net
5. Stanford 3D Indoor Semantic Parsing and Reconstruction Dataset (S3DIS)
6. ModelNet
7. Open3D
AI recommended 7 alternatives but never named zhulf0804/3D-PointCloud. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are essential papers and datasets for 3D point cloud processing in autonomous driving?
you: not recommended
AI recommended (in order):
1. KITTI Vision Benchmark Suite
2. nuScenes Dataset
3. Waymo Open Dataset
4. PandaSet
5. Argoverse 3D Tracking and Motion Forecasting Dataset
6. SemanticKITTI
7. APOLLO SCAPE Dataset
AI recommended 7 alternatives but never named zhulf0804/3D-PointCloud. 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 zhulf0804/3D-PointCloud?
pass
AI named zhulf0804/3D-PointCloud explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts zhulf0804/3D-PointCloud in production, what risks or prerequisites should they evaluate first?
pass
AI named zhulf0804/3D-PointCloud 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 zhulf0804/3D-PointCloud solve, and who is the primary audience?
pass
AI did not name zhulf0804/3D-PointCloud — 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 zhulf0804/3D-PointCloud. 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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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/zhulf0804/3D-PointCloud.svg)](https://repogeo.com/en/r/zhulf0804/3D-PointCloud)

HTML

<a href="https://repogeo.com/en/r/zhulf0804/3D-PointCloud"><img src="https://repogeo.com/badge/zhulf0804/3D-PointCloud.svg" alt="RepoGEO" /></a>

Pro

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zhulf0804/3D-PointCloud — 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