Gaussian Splatting for 3D: From Photos to Real-Time Rendering — Course Syllabus

Name: Gaussian Splatting for 3D: From Photos to Real-Time Rendering
Price: 297 EUR

Reference syllabus for the Gaussian Splatting for 3D: From Photos to Real-Time Rendering 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.

"Train Gaussian Splatting models from your own photos, extract meshes with SuGaR and deploy photorealistic 3D scenes that render in real time."

1. Course Overview

Dimension	Details
Format	Self-paced online course delivered through the 3D Geodata Academy LMS.
Price	€297 (excl. VAT). See section 7 for the legal payment terms.
Learning Objectives	Master splatting theory: Understand 3D Gaussians, the rendering pipeline and training loop well enough to debug. (M1, M2) Extract usable geometry: Convert splat models into clean triangle meshes with SuGaR for CAD and BIM downstream. (M3) Deploy real-time scenes: Compare with NeRF, benchmark quality and ship browser-ready photorealistic 3D. (M4, M5)
Target Audience	Python developers with mid-level skills and a CUDA GPU who want to move past the 3DGS demo and train, debug and deploy photorealistic Gaussian Splatting scenes.
Prerequisites	Working Python notions help. Watch the prerequisites primer →
Estimated Duration	Approximately 12 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 PouxGaussian Splatting is the most exciting development in 3D reconstruction since dense stereo matching, and most tutorials stop at running the paper code. This course gets you to the 100% mark: understand the math, debug quality, extract meshes and ship.

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	Gaussian Splatting Fundamentals Theory of the 3D Gaussian representation and the rendering pipeline.
M2	Training Optimization Quality and speed: adaptive density control and debugging artifacts.
M3	SuGaR Mesh Extraction Extract clean triangle meshes from trained splat models.
M4	NeRF Comparison Hands-on comparison with NeRF: when splatting wins and when NeRF still has the edge.
M5	Real-time Deployment Deploy splat scenes for real-time viewing in the browser.

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 — Gaussian Splatting Fundamentals

Theory of the 3D Gaussian representation and the rendering pipeline.

3D Gaussian representation theory.
Splatting renderer math.
Environment setup (CUDA, PyTorch, 3DGS).
First model training.

M2 — Training Optimization

Quality and speed: adaptive density control and debugging artifacts.

Adaptive density control.
Hyperparameter tuning.
Debugging floaters and noise.
Training speed optimization.

M3 — SuGaR Mesh Extraction

Extract clean triangle meshes from trained splat models.

SuGaR regularization.
Mesh extraction pipeline.
Quality evaluation.
Export to standard formats.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M3 — SuGaR Mesh 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 — NeRF Comparison

Hands-on comparison with NeRF: when splatting wins and when NeRF still has the edge.

NeRF training pipeline.
Quality comparison (PSNR, SSIM, LPIPS).
Speed and memory benchmarks.
Decision framework.

M5 — Real-time Deployment

Deploy splat scenes for real-time viewing in the browser.

Splat viewer setup.
WebGL rendering options.
Scene optimization for delivery.
Portfolio-ready deployed scene.

Expert tip — Dr. Florent PouxTrain on a small indoor scene first. Twenty photos, thirty minutes, then inspect the floaters. You learn more about splatting in that one debugging session than in ten hours of paper reading.

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.