Fast Global Illumination Approximations
on Deep G-Buffers

A newer version of this work appeared in HPG 2016

Michael Mara, NVIDIA and Williams College
Morgan McGuire, NVIDIA and Williams College
Derek Nowrouzezahrai, University of Montreal
David Luebke, NVIDIA

NVIDIA tech report (41 MB PDF)
NVIDIA tech report (brightened) (41 MB PDF)
NVIDIA tech report (low res) (2 MB PDF)
NVIDIA tech report (low res, brightened) (2 MB PDF)
Supplemental results and code (6.8 MB PDF)
Video results (154 MB MP4)
Video results (YouTube)
Demo (154 MB Windows EXE + Source)


Deep Geometry Buffers (G-buffers) combine the fine-scale and efficiency of screen-space data with much of the robustness of voxels. We introduce a new hardware-aware method for computing two-layer deep G-buffers and show how to produce dynamic indirect radiosity, ambient occlusion (AO), and mirror reflection from them in real-time. Our illumination computation approaches the performance of today’s screen-space AO-only rendering passes on current GPUs and far exceeds their quality. Our G-buffer generation method is order-independent, guarantees a minimum separation between layers, operates in a (small) bounded memory footprint, and avoids any sorting. Moreover, to address the increasingly expensive cost of pre-rasterization computations, our approach requires only a single pass over the scene geometry. We show how to apply Monte Carlo sampling and reconstruction to these to efficiently compute global illumination terms from the deep G-buffers.

The resulting illumination captures small-scale detail and dynamic illumination effects and is more substantially more robust than screen space estimates. It necessarily still view-dependent and lower-quality than offline rendering. However, it is real-time, temporally coherent, and plausible based on visible geometry. Furthermore, the lighting algorithms automatically identify undersampled areas to fill from broad-scale or precomputed illumination. All techniques described are both practical today for real-time rendering and designed to scale with near-future hardware architecture and content trends. We include pseudocode for deep G-buffer generation, and source code and a demo for the global illumination sampling and filtering.


Selected Images


  author = {Michael Mara and Morgan McGuire and Derek Nowrouzezahrai  and David Luebke}
  title = {Fast Global Illumination Approximations on Deep G-Buffers},
  month = {June},
  day = {16},
  year = {2014},
  pages = {16},
  institution = {NVIDIA Corporation},
  number = {NVR-2014-001},
  url = {}