Accelerating Monte Carlo Renderers by Ray Histogram Fusion

Please cite the reference article if you publish results obtained with this online demo.

Due to file size constraints, this is a closed demo (the user is not allowed to upload new data). Nevertheless, we encourage the user to compile the code and test it on new data.

Select Data

Click on an image to use it as the algorithm input.

Cornell-path 256spp

Dragons 16spp

Sibenik 128spp

Yeahright 64spp

San-Miguel 256spp

DoF-Dragons 8spp

Dragons-Motion 8spp

Toasters 8spp

Toasters 16spp

Toasters 32spp

Toasters 64spp

Toasters 128spp

Toasters 256spp

Toasters 512spp

Toasters 1024spp

For left to right and up to bottom:

• Cornell box scene generated with Monte Carlo path tracing, 256 samples per pixel; κ=0.8, n_scal=3
• Dragons in the fog scene generated with Photon Mapping, 16 samples per pixel; κ=0.5, n_scal=3
• Sibenik Cathedral scene generated with Monte Carlo path tracing, 128 samples per pixel; κ=0.7, n_scal=3
• Yeahright scene generated with Monte Carlo path tracing, 64 samples per pixel; κ=0.8, n_scal=3
• San Miguel hacienda scene generated with Monte Carlo path tracing, 256 samples per pixel; κ=0.8, n_scal=3
• Dragons scene with Depth-of-field effect generated with Monte Carlo path tracing, 8 samples per pixel; κ=0.7, n_scal=3
• Dragons scene with Motion effect generated with Monte Carlo path tracing, 8 samples per pixel; κ=0.7, n_scal=3
• Toasters scene generated with Monte Carlo path tracing: 8, 16, 32, 64, 128, 256, 512 and 1024 samples per pixel; κ=0.8, n_scal=3