Lukas Ruppert¹, Christoph Kreisl², Nils Blank³, Sebastian Herholz⁴, Hendrik P. A. Lensch^1
1University of Tübingen, ²Robert Bosch GmbH, ³Karlsruhe Institute of Technology, ⁴Intel Coroporation
VMV 2021

Debugging or analyzing the performance of global illumination algorithms is a challenging task due to the complex path-scene interaction and numerous places where errors and programming bugs can occur. We present a novel, lightweight visualization tool to aid in the understanding of global illumination and the debugging of rendering frameworks. The tool provides detailed information about intersections and light transport paths. Users can add arbitrary data of their choosing to each intersection, based on their specific demands. Aggregate plots allow users to quickly discover and select outliers for further inspection across the globally linked visualization views. That information is further coupled with 3D visualization of the scene where additional aggregated information on the surfaces can be inspected in false colors. These include 3D heat maps such as the density of intersections as well as more advanced colorings such as a diffuse transport approximation computed from local irradiance samples and diffuse material approximations. The necessary data for the 3D coloring is collected as a side-product of quickly rendering the image at low sample counts without significantly slowing down the rendering process. It requires almost no pre-computation and very little storage compared to point cloud-based approaches. We present several use cases of how novices and advanced rendering researchers can leverage the presented tool to speed up their research.

Paper [PDF] [EG Library]
Code [GitHub]

@inproceedings {ruppert2021emca,
  booktitle = {Vision, Modeling, and Visualization},
  editor = {Andres, Bjoern and Campen, Marcel and Sedlmair, Michael},
  title = {{EMCA: Explorer of Monte Carlo based Algorithms}},
  author = {Ruppert, Lukas and Kreisl, Christoph and Blank, Nils and Herholz, Sebastian and Lensch, Hendrik P. A.},
  year = {2021},
  publisher = {The Eurographics Association},
  ISBN = {978-3-03868-161-8},
  DOI = {10.2312/vmv.20211377}
}

This work has been partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645 and SFB 1233, TP 02 - Project number 276693517. It was supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS18039A.