Applied Graph Theory for 3D Scene Graph Intelligence — Course Syllabus

Name: Applied Graph Theory for 3D Scene Graph Intelligence
Price: 197 EUR

Reference syllabus for the Applied Graph Theory for 3D Scene Graph Intelligence 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.

"Turn segmented point clouds into queryable scene graphs — nodes, typed edges, VLM enrichment and natural-language queries with OpenUSD export."

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	Model 3D as graphs: Master the exact subset of graph theory that maps to scenes: nodes, edges, adjacency, traversal. (M1) Build the graph from geometry: Compute OBBs, centroids and typed edges (above, inside, adjacent) from segmented clouds. (M2, M3) Query and enrich with AI: Plug a VLM for open-vocabulary labels and an LLM for natural-language graph queries. (M4, M5)
Target Audience	Engineers comfortable with point clouds, Open3D and basic Python who want to turn segmented 3D data into queryable scene graphs for spatial AI agents.
Prerequisites	Working Python notions help. Watch the prerequisites primer →
Estimated Duration	Approximately 10 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 because I kept watching teams ship beautiful segmentation pipelines that could not answer a single useful question. Labels without structure are pretty colors. Wrap them in a graph and everything changes: you can query, reason, plan.

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	Graph Theory for 3D The exact subset you need: nodes, edges, adjacency and NumPy/NetworkX representations.
M2	Scene Graph Construction Turn segmented point clouds into node sets with geometric attributes.
M3	Relationship Extraction Compute typed edges from geometric predicates and contact tests.
M4	VLM Semantic Enrichment Render clusters, run a Visual Language Model and reconcile labels.
M5	Querying and Export Natural-language queries via LLMs, Cypher traversals and OpenUSD export.

Why this structure, Dr. Florent PouxEach of the 5 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 — Graph Theory for 3D

The exact subset you need: nodes, edges, adjacency and NumPy/NetworkX representations.

Graph theory primer (the useful parts).
Directed vs undirected, typed vs untyped graphs.
Adjacency representations in NumPy.
NetworkX for rapid prototyping.
Mapping 3D objects to nodes.

M2 — Scene Graph Construction

Turn segmented point clouds into node sets with geometric attributes.

Clustering into scene objects.
OBB and AABB computation.
Node attribute engineering.
Hierarchical graph construction.
Persisting to disk and to Neo4j.

M3 — Relationship Extraction

Compute typed edges from geometric predicates and contact tests.

Spatial relationship predicates.
Contact and containment tests.
Proximity graphs and gating.
Typed edge schemas.
Validation on real indoor scans.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M3 — Relationship Extraction, 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 — VLM Semantic Enrichment

Render clusters, run a Visual Language Model and reconcile labels.

Cluster rendering pipeline.
VLM prompting for 3D objects.
Open-vocabulary labels and reconciliation.
Confidence fusion strategies.
Cost and latency profiling.

M5 — Querying and Export

Natural-language queries via LLMs, Cypher traversals and OpenUSD export.

Cypher and SPARQL basics.
LLM-powered natural language querying.
OpenUSD export for Omniverse.
Graph visualization for debugging.
Portfolio-ready scene graph demo.

Expert tip — Dr. Florent PouxStart with NetworkX, not Neo4j. The graph layer is conceptually the same and you skip two days of Docker debugging. Migrate to Neo4j only when you actually need persistence across processes.

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.