REPOGEO REPORT · LITE

hustvl/MapTR

Default branch main · commit a6872d8d · scanned 5/21/2026, 8:03:10 PM

GitHub: 1,505 stars · 241 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)

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

AI VISIBILITY SCORE

59 /100

Needs work

Category recall

1 / 2

Avg rank #4.0 when recommended

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 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 hustvl/MapTR, 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 concise problem/solution statement after the main title

Why:

CURRENT

<div align="center">
<h1>MapTR </h1>
<h3>An End-to-End Framework for Online Vectorized HD Map Construction</h3>

COPY-PASTE FIX

<div align="center">
<h1>MapTR </h1>
<h3>An End-to-End Framework for Online Vectorized HD Map Construction</h3>
</div>

MapTR is a cutting-edge deep learning model that provides an end-to-end, transformer-based solution for online vectorized HD map construction, recognized as an ICLR'23 Spotlight, ECCV'24, and IJCV'24 publication.

mediumhomepage#2
Add the arXiv preprint link as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2208.14437
```

lowreadme#3

Add a 'Key Differentiators' section to the README

Why:

COPY-PASTE FIX

## Key Differentiators

MapTR stands apart from traditional mapping tools and general autonomous driving platforms by focusing on an *end-to-end, learning-based approach* for *online vectorized HD map construction*. Unlike systems that rely on manual annotation, pre-built maps, or complex multi-stage pipelines, MapTR directly predicts structured 3D map elements from raw sensor data using a transformer architecture, making it suitable for dynamic and real-time applications.

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

1 / 2

50% of queries surface hustvl/MapTR

Avg rank

#4.0

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenStreetMap (OSM)

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenStreetMap (OSM) · recommended 2×
ApolloAuto/apollo · recommended 1×
AutowareFoundation/autoware.auto · recommended 1×
fzi-forschungszentrum-informatik/Lanelet2 · recommended 1×
cartographer-project/cartographer · recommended 1×

CATEGORY QUERY
How to generate high-definition vectorized maps in real-time for autonomous driving?
you: not recommended
AI recommended (in order):
1. Apollo (Baidu Apollo) (ApolloAuto/apollo)
2. Autoware.Auto (AutowareFoundation/autoware.auto)
3. OpenStreetMap (OSM)
4. Lanelet2 (fzi-forschungszentrum-informatik/Lanelet2)
5. Google Cartographer (cartographer-project/cartographer)
6. PCL (Point Cloud Library) (PointCloudLibrary/pcl)
7. OpenCV (Open Source Computer Vision Library) (opencv/opencv)
8. NVIDIA DriveWorks
AI recommended 8 alternatives but never named hustvl/MapTR. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking an end-to-end solution for vectorized HD map creation using transformer architectures.
you: #4
AI recommended (in order):
1. Waymo Open Dataset
2. Waymo's HD Map Creation Pipeline
3. VectorNet
4. MapTR ← you
5. nuScenes Dataset
6. DETR
7. Mask2Former
8. VectorMapNet
9. ApolloScape Dataset
10. Baidu Apollo's Map Learning
11. OpenStreetMap (OSM)
12. ViT
13. Swin Transformer
14. CARLA Simulator
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 hustvl/MapTR?
pass
AI named hustvl/MapTR explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts hustvl/MapTR in production, what risks or prerequisites should they evaluate first?
pass
AI named hustvl/MapTR 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 hustvl/MapTR solve, and who is the primary audience?
pass
AI named hustvl/MapTR explicitly
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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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/hustvl/MapTR.svg)](https://repogeo.com/en/r/hustvl/MapTR)

HTML

<a href="https://repogeo.com/en/r/hustvl/MapTR"><img src="https://repogeo.com/badge/hustvl/MapTR.svg" alt="RepoGEO" /></a>

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hustvl/MapTR — 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