TL;DR: Brand new course (lectures freshly recorded Fall 2025) bridging classical graphics with the modern neural stack: NeRF, 3D Gaussian Splatting, PBD, and Diffusion. Generous grading (97% A rate among non-withdrawals), reasonable workload, and every assignment produces something visually cool you can actually show people. 9/10, strongly recommend if you have any interest in graphics, generative 3D, or physics simulation.

Background (for calibration)

Comfortable with Python and C-style syntax, advanced NN. Minimal CG experience.

What makes it stand out

Most OMSCS courses have lectures from 2017-ish that show their age. This one was recorded Fall 2025 and the reading list is almost entirely post-2020 papers (NeRF '20, 3DGS '23, score-based diffusion, etc.). It's the closest thing to a "current state of the field" survey OMSCS has right now for graphics + neural rendering.

Course content — 6 modules

1. Foundation of Graphics — ray tracing, Phong, GPU basics. Ramp-up for folks without CG background.
2. Signed Distance Fields — primitives, boolean ops, sphere marching, neural SDF.
3. Neural Radiance Fields — radiometry, volumetric rendering, NeRF, accelerated variants.
4. 3D Gaussian Splatting — splat math, footprint, 2D-GS, 3D-GS.
5. Position-Based Dynamics — particle sim, PBD, differentiable physics, neural physics.
6. Generative Models — diffusion, score/flow matching, geometry/video/world models.

Modules 2–4 are the heart of the course: how to replace the traditional grid/mesh pipeline with neural representations.

Grading

• Assignments: 64% (8% × 8)
• Midterm: 16% — 1 hour, open book, take it any time within a week. Prof gives review material in advance. Bottom 25% still got full marks (based on Canvas).
• Final Project: 16%
• Participation: 4%x

Grading is generous - 97% of non-withdrawals got an A (based on official GaTech catalog site).

Assignments (8 total)

GLSL shaders + PyTorch training notebooks. ~100–200 net LOC per assignment (excluding boilerplate).

• A1 — Ray Tracer: ray-sphere/box intersections, Phong, shadow rays, recursive reflection
• A2a — SDF Rendering: primitives, boolean ops, sphere marching
• A2b — Neural SDF: MLP fits mesh SDF using DeepSDF-style narrowband loss + clamp, render with ray marching
• A3a — Volumetric Rendering: Beer-Lambert, RTE, front-to-back discrete integration
• A3b — NeRF: positional encoding + MLP, train a tiny NeRF on lego/chair/drums/hotdog
• A4 — 2D Gaussian Splatting: parameterize 2D Gaussians, gradient-descent fit a target image
• A5 — PBD: position-based dynamics, constraint projection + Verlet integration
• A6 — Diffusion: forward noising, score matching, train a minimal generative model

Final Project

Two tracks: Creative (build a 3D scene, scored on artistry + tech) or Technical (mini research + report). I did Creative — built a Schwarzschild black hole renderer (geodesic integration + the A3a volumetric stack), took ~15 hours total. Reusing your own assignment scaffolding is encouraged.

Workload & difficulty

~10–15 hrs/week for an average student, spiking on assignment weeks.

Self-rated difficulty:

• 4–5 / 10 if you're comfortable with Python + C-style syntax and machine learning
• 7–8 / 10 otherwise (the math + shader combo is a real ramp)

Apple Silicon / integrated GPU is enough for the WebGL parts. Neural training runs on Google Colab.

Tips

• Save each assignment's fragment.glsl — you'll likely reuse one as your final project scaffold
• Skim the OG NeRF and 3DGS papers before those modules; lectures hit harder if you've seen the figures once
• Start thinking of your final project topic early.

The honest downside

The later assignments (roughly A4 onward — 3DGS, PBD, Diffusion) feel noticeably shallower than the earlier ones. Not really the course's fault — a faithful end-to-end implementation of any of those topics easily blows past the time and GPU hours you can reasonably ask of a single homework. You get the core math, a working minimal pipeline, and enough scaffolding to actually read the papers, but you're not training a full-resolution 3DGS scene or a serious diffusion model in your A6 notebook. Treat the back half of the course as well-scoped on-ramps for your own deep dives, not as production-grade implementations.

Bottom line

9/10. The most current course on the platform if you care about graphics, generative 3D, or simulation. Lenient grading and a real portfolio artifact at the end. If you're shopping for a Spring elective and any of these topics catch your eye, just take it.

Happy to answer questions in the comments.