All the dates are for the Czech course
Playlist
for the lectures (2021/2022).
Playlist
for the labs (2022/2023).
Course content, additional sources, Ray-tracing principles revisited.
Video:
Lecture 1 (2021/22).
Credit system, programming environment (C# environment,
Git repository RT007, MS Visual Studio),
Git repository RT004,
ray-tracing example: 048rtmontecarlo-script.
Video:
Lab 1 (2022/23) – lab credit,
demo, RT004 repository, steps and checkpoints.
Shading basics, continuous shading. Definition of BRDF,
physics, general BRDF concepts...
Video:
Lecture 2 (2021/22).
Lab credit details, more about the RT004 repo. See
lab page for details.
Video:
Lab 2 (2021/22) – more
RT004 details, Checkpoint 1
General BRDF concepts revisited, Fresnel functions, microfacet models:
Cook-Torrance, Oren-Nayar, looking for better microfacet distributions
D(h) and geometric factors G, Lafortune's lobe model, Schlick's improvements,
subsurface scattering...
Video:
Lecture 3 (2021/22).
Shading interpolation demo (Gouraud, Phong).
Ray-tracer implementation: Camera (primary ray generator),
Solid/Shape (ray representation, intersection computation...)
Video:
Lab 3 (2022/23) – shading
interpolation demo, RT004...
Ray-scene intersection basics: planar shapes, convex polyhedron, implicit and algebraic surfaces,
general and rotational quadrics, sphere (geometric solution), torus, surface of revolution,
CSG representation.
Video:
Lecture 4 (2021/22).
Ray-tracer implementation: Solid/Shape revisited (primary computation
vs. additional data = normal vector, TXT coordinates...),
BRDF & materials (cooperating objects, materials are associated with
scene objects/nodes as attributes...),
light sources (point/directional).
image synthesis for ray-based rendering – camera, scene, light sources,
computing colors of all pixels in the picture.
Video:
Classification of acceleration techniques, bounding solid, bounding efficiency,
bounding-volume-hierarchy (BVH), efficiency and construction,
SAH heuristics.
Video:
Lecture 5 (2021/22).
RT004 future:
textures, OOP, scene file-format, acceleration (preprocessing),
parallelism...
Video:
Space dividing methods revisited: grid, 3DDDA, octree, KD-tree, subdivision
approaches, adaptive tree pass.
[Directional acceleration techniques, cube directory, light buffer,
ray coherency, projection plane directory, generalized rays]
Bezier surfaces: Geometric method (Newtonish), De Casteljau subdivision...
Textures in ray-tracing – 2D and 3D textures, table (bitmap) vs. procedural texture,
table interpolations. "Bump-texture" (normal map), stochastic textures - introduction.
Video:
Lecture 6 (2021/22).
More notes on OOP and shadows, recursive ray-tracing (shade()
function). New scene example (120° viewing angle).
Texture filtering examples, simple bump-texture example.
Video:
Synthetic noise functions (white noise, interpolation and convolution methods), Perlin noise, Lewis sparse
convolution, turbulence, application of noise functions in texture synthesis: wood, marble.
More applications of noise functions (water surface simulation, flame simulation).
Video:
Lecture 7 (2021/22).
More usage of noise functions (water surface simulation, flame simulation).
Video:
Basics of sampling theory, anti-aliasing in R-T context, spatial/temporal alias,
Anti-aliasing by numeric quadrature, sampling method survey (regular, random sampling, jittering,
"N-rooks" sampling, Poisson disc sampling, Mitchell's algorithm, deterministic
algorithms).
Video:
Lecture 8 (2021/22).
Video:
Adaptive sampling, supersampling criteria, practical examples.
Distributed ray-tracing: glossy reflections and refractions,
soft shadows, depth-of-field simulation, motion blur,
light dispersion. Monte-Carlo quadrature, examples.
Multi-dimensional sampling, hidden sampling.
Video:
Lecture 9 (2021/22).
Extensions t01-t04.
Video:
Basic radiometric terms, flux, radiance, irradiance,
solid angles, BRDF, Kajiya's rendering equation.
Problem discretization (FEM), system of linear equations for radiosity.
Video:
Lecture 10 (2021/22).
Extensions t05-t08.
Video:
Monte Carlo integration: introduction, primary and secondary estimates,
variance, stratified sampling, importance sampling, combined estimators,
examples
Video:
Lecture 11 (2021/22) –
Monte Carlo integration (the simplest task)
Integral equations, random walks, Russian roulette,
next event estimation (NEE)...
Rendering equation revisited (Kajiya), symbolic light transport
description (regular expressions)
Video:
Lecture 12 (2021/22) –
Monte Carlo estimation of Fredholm integral equation system
Path-tracing (random paths), bidirectional path tracing, NEE, examples.
Video:
Lecture 13 (2021/22) –
Monte-Carlo rendering, Path tracing, Light tracing, Bidirectional path tracing
Copyright (C) 2001-2023 J.Pelikán, last change: 2023-04-25 07:21:59 +0200 (Tue, 25 Apr 2023)