Computer Graphics and Visualization Seminar on Thursday, March 7, 2019, 15:45-17:45h, at EWI-Lecture hall Chip.
The program features the following two speakers:
Remi van der Laan
Title: Exploiting Coherence in Time-Varying Voxel Data
Abstract: We encode time-varying voxel data for efficient storage and streaming. We store the equivalent of a separate sparse voxel octree for each frame, but utilize both spatial and temporal coherence to reduce the amount of memory needed. We represent the time-varying voxel data in a single directed acyclic graph with one root per time step. In this graph, we avoid storing identical regions by keeping one unique instance and pointing to that from several parents. We further reduce the memory consumption of the graph by minimizing the number of bits per pointer and encoding the result into a dense bitstream.
Michiel van Spaendonck
Title: Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination
Abstract: We introduce a reconstruction algorithm that generates a temporally stable sequence of images from one path-per-pixel global illumination. To handle such noisy input, we use temporal accumulation to increase the effective sample count and spatiotemporal luminance variance estimates to drive a hierarchical, image-space wavelet filter. This hierarchy allows us to distinguish between noise and detail at multiple scales using local luminance variance.
Physically based light transport is a long-standing goal for real-time computer graphics. While modern games use limited forms of ray tracing, physically based Monte Carlo global illumination does not meet their 30Hz minimal performance requirement. Looking ahead to fully dynamic real-time path tracing, we expect this to only be feasible using a small number of paths per pixel. As such, image reconstruction using low sample counts is key to bringing path tracing to real-time. When compared to prior interactive reconstruction filters, our work gives approximately 10x more temporally stable results, matches reference images 5-47% better (according to SSIM), and runs in just 10ms (+- 15%) on modern graphics hardware at 1920×1080 resolution.