1. Practical Path Guiding for Efficient Light-Transport Simulation
- Author
-
Thomas Müller, Markus Gross, and Jan Novák
- Subjects
Theoretical computer science ,Computer science ,02 engineering and technology ,computer.software_genre ,Molecular graphics ,Rendering (computer graphics) ,Computer graphics ,Vector graphics ,0202 electrical engineering, electronic engineering, information engineering ,Quadtree ,Computer graphics lighting ,Binary tree ,Software rendering ,Scientific visualization ,020207 software engineering ,Computer Graphics and Computer-Aided Design ,Graphics pipeline ,Real-time computer graphics ,Graphics software ,Radiance ,Memory footprint ,020201 artificial intelligence & image processing ,General-purpose computing on graphics processing units ,2D computer graphics ,Algorithm ,computer ,3D computer graphics - Abstract
We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms. Inspired by existing path-guiding algorithms, our method learns an approximate representation of the scene's spatio-directional radiance field in an unbiased and iterative manner. To that end, we propose an adaptive spatio-directional hybrid data structure, referred to as SD-tree, for storing and sampling incident radiance. The SD-tree consists of an upper part-a binary tree that partitions the 3D spatial domain of the light field-and a lower part-a quadtree that partitions the 2D directional domain. We further present a principled way to automatically budget training and rendering computations to minimize the variance of the final image. Our method does not require tuning hyperparameters, although we allow limiting the memory footprint of the SD-tree. The aforementioned properties, its ease of implementation, and its stable performance make our method compatible with production environments. We demonstrate the merits of our method on scenes with difficult visibility, detailed geometry, and complex specular-glossy light transport, achieving better performance than previous state-of-the-art algorithms.
- Published
- 2017