3D Segmentor OS — Course Syllabus

Name: 3D Segmentor OS
Price: 1497 EUR

Reference syllabus for the 3D Segmentor OS course delivered by the 3D Geodata Academy. It defines the learning objectives, audience, technical requirements, the module-by-module program, the assessment scheme, the results indicators and the legal terms of purchase.

"The complete 3D segmentation operating system: from RANSAC and region growing to SegmentAnything-3D, scene graphs and a production WebGL app."

1. Course Overview

Dimension	Details
Format	Self-paced online course delivered through the 3D Geodata Academy LMS.
Price	€1 497 (excl. VAT). See section 7 for the legal payment terms.
Learning Objectives	Master classical 3D segmentation: Apply RANSAC, region growing, Euclidean clustering, marching cubes and PCA-based methods on real point clouds. (M3, M4) Operate modern AI segmentation: Use SegmentAnything 3D, scene graph generation, change detection and unsupervised segmentation. (M5, M6) Build a 3D processing app: Ship a 3D processing framework with WebGL visualisation and labelling support. (M7)
Target Audience	Engineers and researchers who need a complete 3D segmentation toolbox: classical algorithms, deep learning and modern foundation models.
Prerequisites	Working Python notions help. Watch the prerequisites primer →
Estimated Duration	Approximately 28 hours of focused work. Fully asynchronous.
Access	Direct enrolment via the 3D Geodata Academy. A 14-day legal cooling-off period applies.
Accessibility & Disability	All courses are open to learners with disabilities. A dedicated referent reviews each request to put the right pedagogical and technical adjustments in place. Referent: Dr. Florent Poux — howto@learngeodata.eu.
Contact	Dr. Florent Poux — howto@learngeodata.eu 3D Geodata Academy

A note from Dr. Florent PouxSegmentation is where most 3D AI projects either ship value or quietly die. The OS approach here is deliberate: you get a forge of methods, then learn when to use which — instead of betting the whole project on one model.

2. Technical Stack & Pedagogical Means

Software stack: Python and the standard 3D ecosystem (Open3D, Laspy, NumPy, PyTorch, CloudCompare, Meshroom — adapted to the course focus).
Hardware: any modern laptop (Windows, macOS, Linux). GPU recommended for deep learning modules.
Datasets: reference datasets and notebooks provided.
Infrastructure: proprietary LMS with 24/7 access, automated quizzes, progress tracking and digital course materials.
Modality: alternates theory and hands-on practice — code, real datasets, software use and shippable project deliverables.
Theory resources: video lessons, written handouts, curated research articles and PhD-level references.

3. Course Structure

Module	Title & Focus
M0	Roadmap Course map, learning rhythm, dataset access.
M1	3D Python foundations Building the environment and data primitives.
M2	3D Object Detection Detection-first systems for industrial scenes.
M3	3D AI Semantic Segmentation Supervised pipelines and scope vs performance.
M4	3D Algorithm Forge Classical algorithms portfolio.
M5	Foundation models for 3D SAM 3D and unsupervised segmentation.
M6	Create 3D Assets Production app, framework, WebGL viewer.

Why this structure, Dr. Florent PouxThe 7 modules are ordered the way I build systems in production, not the way a textbook would list them. If you are new to the topic, follow the sequence. If you are experienced, you can jump ahead, but come back to M6 before the oral defence. That's where everything ties together.

M0 — Roadmap

Course map, learning rhythm, dataset access.

3D Segmentor roadmap and learning path.

M1 — 3D Python foundations

Building the environment and data primitives.

Building foundations: 3D Python.

M2 — 3D Object Detection

Detection-first systems for industrial scenes.

Building systems: 3D object detection.

M3 — 3D AI Semantic Segmentation

Supervised pipelines and scope vs performance.

Scope x perfs: 3D AI semantic segmentation.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M3 — 3D AI Semantic Segmentation, stop and apply what you've learned to a dataset you actually care about. The back half of the course goes faster when the first half sits on a real example, not a toy one.

M4 — 3D Algorithm Forge

Classical algorithms portfolio.

Region growing for 3D segmentation.
Euclidean clustering (graph theory).
Marching cubes (3D meshing).
RANSAC (shape detection).
PCA + Random Forests.
3D scene graph generation.
3D change detection.
Large scan (.e57) processing.

M5 — Foundation models for 3D

SAM 3D and unsupervised segmentation.

Unsupervised segmentation: 3D SegmentAnything.
3D data labelling pipelines.
3D AI generation: Text/Image-to-3D.

M6 — Create 3D Assets

Production app, framework, WebGL viewer.

3D processing app: framework.
3D app: execution and implementation.
3D visualisation experience (WebGL).

Expert tip — Dr. Florent PouxModule 4 is the gold mine. Classical algorithms still beat deep learning on a surprising number of industrial datasets.

4. Assessment, Certificate & Grading

The OS program tier includes a project deliverable and an oral defence in addition to the quiz-based progression.

Stage	Activity	Validation
Before the course	Positioning quiz.	Informative.
During the course	End-of-module quizzes.	Score ≥ 70 % per quiz.
End of the course	Final quiz.	Score ≥ 80 %.
End of the course	Project deliverable submission.	Reviewed against published rubric.
End of the course	Oral defence with Dr. Florent Poux (45 min).	Score ≥ 15 / 25.

Conditions to obtain the certificate

Validate every end-of-module quiz (≥ 70 %).
Validate the final quiz (≥ 80 %).
Submit the project deliverable and pass the oral defence (≥ 15/25).

Grading scale

Certified — Pass: all criteria validated.
Certified with Merit: oral defence ≥ 18/25.
Certified with Distinction: oral defence ≥ 22/25 and quiz average ≥ 90 %.

Successful learners receive the OS certificate and the Alumni digital badge.

Accessibility & disability: all evaluations can be adapted (extended time, alternative formats, oral or written substitution) on request to the disability referent howto@learngeodata.eu.

5. Course Results & Quality Indicators

3D Geodata Academy publishes its course performance indicators transparently. Figures below cover this course and are updated at the end of each session.

Indicator	Current Result	Target
Number of enrolled learners	Data being consolidated	Continuous growth
Satisfaction rate	Data being consolidated	> 95 %
Success rate (certificate obtained)	Data being consolidated	> 85 %
Drop-out / interruption rate	Data being consolidated	< 5 %
Recommendation rate	Data being consolidated	> 90 %

Indicators consolidated from in-LMS quizzes and end-of-course satisfaction surveys. Last update: April 2026.

6. Next Step

This OS course gives you a complete operational system. To go further with structured mentorship and a wider curriculum, secure your spot below or join the 3D AI Accelerator.

🛒 Secure your spot 🚀 Join the 3D AI Accelerator

The 3D AI Accelerator adds direct mentorship with Dr. Florent Poux, full access to the complete course library (20+ courses), monthly analytics on the 3D spatial AI ecosystem, curated research papers and the private job board with reviews and notes on which roles are worth pursuing.

7. Legal, Accessibility & Purchase Conditions

Digital accessibility

3D Geodata Academy is committed to making its content accessible to learners with disabilities. A dedicated referent oversees content accessibility (captioning, transcripts, screen-reader friendly layouts, alternative evaluation formats). Contact: howto@learngeodata.eu.

Access for learners with disabilities

Every course can be adapted upon request. A short questionnaire at enrolment captures the pedagogical and technical adjustments needed. Disability referent: Dr. Florent Poux — howto@learngeodata.eu.

Payment terms — IP-protection caution & 14-day retraction

Because course content is delivered digitally and immediately accessible, the purchase is structured as follows to protect the intellectual property while preserving your legal right of retraction:

At checkout, the payment is collected as a caution / security deposit for IP infringement protection. Access to the LMS is granted immediately.
The deposit is held for 14 calendar days, matching the legal cooling-off period for distance contracts.
If you exercise your right of retraction within those 14 days and have not consumed a substantial part of the content, the deposit is refunded in full.
At the expiry of the 14-day period, in the absence of a retraction request, the deposit is automatically converted into the actual payment.

By purchasing, the learner acknowledges that the course materials are original works protected by copyright. Any reproduction, redistribution or commercial reuse without prior written consent is prohibited.

Data protection (GDPR)

Personal data is processed for the sole purpose of delivering the course, tracking progress, issuing the certificate and providing customer support. Access, rectification or deletion: howto@learngeodata.eu.

General terms of sale

Full general terms of sale are available on request and joined to every order confirmation.