3D Unsupervised Segmentation: Automate Labelling Without Ground Truth — Course Syllabus

Name: 3D Unsupervised Segmentation: Automate Labelling Without Ground Truth
Price: 197 EUR

Reference syllabus for the 3D Unsupervised Segmentation: Automate Labelling Without Ground Truth 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.

"Segment new point clouds with zero annotated data — SegmentAnything 3D, region growing, Euclidean clustering and RANSAC combined into one hybrid pipeline."

1. Course Overview

Dimension	Details
Format	Self-paced online course delivered through the 3D Geodata Academy LMS.
Price	€197 (excl. VAT). See section 7 for the legal payment terms.
Learning Objectives	Deploy SAM in 3D: Adapt Meta's SAM to point clouds via 2D projection and lift results back to 3D. (M1) Master classical clustering: Use region growing, Euclidean clustering and RANSAC for planar, disconnected and primitive extraction. (M2, M3) Ship a hybrid pipeline: Combine foundation models and classical methods and output a labelled dataset ready for supervised training. (M4)
Target Audience	Python developers comfortable with point clouds who need to segment new LiDAR sites without labelled training data, using SAM-3D, RANSAC and classical clustering.
Prerequisites	Working Python notions help. Watch the prerequisites primer →
Estimated Duration	Approximately 8 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 PouxI built this course after watching clients burn six-figure annotation budgets that produced models worse than a well-tuned unsupervised baseline. Every technique in here comes from real projects where we skipped labelling and shipped faster.

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
M1	SegmentAnything 3D Project to 2D, segment with SAM, lift masks back to 3D with zero training.
M2	Region Growing & Clustering Two workhorse algorithms for smooth surfaces and disconnected objects.
M3	RANSAC Shape Detection Robust plane, cylinder and sphere extraction in cluttered scenes.
M4	End-to-end Pipeline Hybrid architecture that ships a labelled dataset ready for supervised training.

Why this structure, Dr. Florent PouxEach of the 4 modules ends with a quiz, and the quizzes are cumulative. Don't skip a module just because you think you know it. The gaps you didn't know you had show up in the final quiz.

M1 — SegmentAnything 3D

Project to 2D, segment with SAM, lift masks back to 3D with zero training.

SAM theory and architecture.
3D to 2D projection strategies.
SAM inference on projected views.
Lifting 2D masks back to 3D.
Multi-view consistency.

M2 — Region Growing & Clustering

Two workhorse algorithms for smooth surfaces and disconnected objects.

Region growing from seed points.
Normal and curvature thresholds.
Euclidean clustering with KD-trees.
Parameter tuning strategies.
Benchmark evaluation.

M3 — RANSAC Shape Detection

Robust plane, cylinder and sphere extraction in cluttered scenes.

RANSAC fundamentals.
Plane extraction on ground surfaces.
Cylinder and sphere fitting.
Sequential RANSAC for multiple primitives.
Combining with clustering.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M3 — RANSAC Shape Detection, 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 — End-to-end Pipeline

Hybrid architecture that ships a labelled dataset ready for supervised training.

Hybrid architecture design.
Decision tree for method selection.
3D data labelling workflow.
Dataset export and conversion.
Portfolio-ready project.

Expert tip — Dr. Florent PouxStart with the classical methods, not SAM. Region growing on a clean plane beats SAM on a cluttered scene every single time. SAM is the specialist. Clustering is the workhorse.

4. Assessment, Certificate & Grading

This is a standalone course: there is no project to defend and no oral examination. Evaluation is fully quiz-based, automated through the LMS.

Stage	Activity	Validation
Before the course	Optional positioning quiz to calibrate prior knowledge.	Informative — no minimum score.
During the course	End-of-module quiz (one per module, 10 to 15 questions).	Score ≥ 70 % per quiz.
End of the course	Final quiz covering all modules.	Score ≥ 80 %.

Conditions to obtain the certificate

Validate every end-of-module quiz with a score of 70 % or more.
Validate the final quiz with a score of 80 % or more.
Quizzes can be retaken without limit; the latest score is retained.

Grading scale

Pass: all quizzes ≥ 70 % and final ≥ 80 %.
Pass with Merit: average across all quizzes ≥ 85 %.
Pass with Distinction: average across all quizzes ≥ 92 %.

Successful learners receive the course certificate (PDF + verifiable digital badge) and join the Alumni registry.

Accessibility & disability: all evaluations can be adapted (extended time, alternative formats, oral or written substitution, screen-reader friendly versions) 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 course gives you the operational base. 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.