# 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