AI-Powered 3D Vision for Generative Intelligence — Course Syllabus

Name: AI-Powered 3D Vision for Generative Intelligence
Price: 197 EUR

Reference syllabus for the AI-Powered 3D Vision for Generative 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.

"From a single image to a 3D model — depth estimation, monocular reconstruction and generative 3D pipelines you can run today."

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	Run monocular depth pipelines: Apply MiDaS, DepthAnything and DepthAnything v2 to your own images and integrate depth into reconstruction. (M2) Generate 3D from images: Use TRELLIS, Stable Diffusion 3D extensions and image-to-3D models to produce voxels, meshes and point clouds. (M3) Build hybrid reconstructions: Combine neural methods with classical SfM and pick the right tool per job. (M1, M4)
Target Audience	Engineers and researchers with a working Python background who want to add neural 3D vision — depth estimation, monocular reconstruction and generative 3D — to an existing photogrammetry toolkit.
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 spent two years tracking neural 3D vision before I trusted any of it enough to put in a course. This is the concentrated version of that research, stripped of hype and focused on what runs on your data today.

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	AI for 3D Applications Where neural 3D vision is today, what works in production and when to stick with geometry.
M2	Depth Estimation Monocular depth from MiDaS to DepthAnything v2, integrated into 3D reconstruction.
M3	Image-to-3D Voxels, meshes and point clouds generated from a single image or prompt.
M4	Hybrid Pipelines Mix neural and classical methods and build a decision framework for production.

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 — AI for 3D Applications

Where neural 3D vision is today, what works in production and when to stick with geometry.

Overview of neural networks for 3D vision.
AI for 3D applications landscape.
When to use neural vs classical methods.
Pre-trained model zoo walkthrough.

M2 — Depth Estimation

Monocular depth from MiDaS to DepthAnything v2, integrated into 3D reconstruction.

MiDaS deep dive.
DepthAnything v1 and v2.
Monocular to metric depth.
Integrating depth into 3D reconstruction.

M3 — Image-to-3D

Voxels, meshes and point clouds generated from a single image or prompt.

Monocular image to 3D voxels.
Generative AI for 3D reconstruction.
Microsoft TRELLIS deep dive.
Stable Diffusion / DALL-E for image-to-3D.
Quality metrics for generative outputs.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M3 — Image-to-3D, 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 — Hybrid Pipelines

Mix neural and classical methods and build a decision framework for production.

Hybrid neural-classical reconstruction.
Decision framework for tool selection.
Quality validation and metrics.
Portfolio-ready hybrid project.

Expert tip — Dr. Florent PouxDo not pick between neural and classical. Pick the right one per job. The decision framework in Module 4 is worth more than any single model I cover in Module 2 or 3.

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.