moon
sun

Sample-based Monte Carlo Denoising using a Kernel-Splatting Network

Status
Read
Field
Monte Carlo Rendering
Denoising
Ray Tracing
Conference / Journal
SIGGRAPH
Year
2019
Link
Created
2020/08/31 00:56
Novel point of SBMC is...
1.
Sample-wise reconstruction allows more expressive contributions for reconstruction than pixel-wise reconstruction
2.
Splatting helps the model to be trained without shortcut (reducing range of kernel to reduce loss) compared to gathering.
3.
Splatting is permutation-invariant, which allows sample-wise reconstruction to show better performance.
1.
Scene generation
Geometry β†’ ShapeNet, SunCG dataset
Camera β†’ FOV, depth-of-field effect , shutter speed, motion blur (translation motion)
Material β†’ materials from PBRT v2, bias to diffuse materials, random texture & bumps & UV scaling, Describable Textures Dataset
Lighting β†’ HDRI Haven environmental maps
Rejection sampling β†’ remove too much simple dataset
2.
Interesting Results
By maintaining albedo, it could denoise effects such as motion blur & defocus
Beneficial on low-sample, time increases linearly to samples
Scene complexity does not affect the runtime.