proseminar/presentation/notes.md

Notes diffable Monte carlo RT

Raytracing formula

• geometry term discussed later
• Emission + All light reflected towards point
• Yields infinite recursion -> not calculable

Visualization

• Explain image
• No indirect lighting!
• Output image is what we would expect (explain shade)

Differentiable rendering

• That function is dependent on renderer
• Renderer needs to be differentiable

Importance

• Inversely render complex indoor scenes
• "Fool" neural network
• Real time realistic shading in AR
• Application in maritime research

Adversarial image generation

• Example for classification on slide 2!
• Fool neural netweork into wrongly classifying input data
  • Optimize Image into wrong class

Why differentiable rendering is hard

• Example later
• geometry term explanation later

Former methods visualization

• Plane lit by a point light source.
• gradient with respect to the plane moving right
• light source remains static => the gradient should only be \ne 0 at the boundaries
• OpenDR and Neural not able to correctly calculate the gradients
  • they are based on color buffer differences

Edge sampling

• Approximate point lights using small area lights
• Specular => angle of incidence = angle of light reflected
• only lambertian materials

Edge Sampling - Math Background

• Heaviside step functions in f_i(x,y)

Inverse Rendering - Results in this paper

• ADAM: talk by Mr. Wu