3 research outputs found
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
The variance reduction speed of physically-based rendering is heavily
affected by the adopted importance sampling technique. In this paper we propose
a novel online framework to learn the spatial-varying density model with a
single small neural network using stochastic ray samples. To achieve this task,
we propose a novel closed-form density model called the normalized anisotropic
spherical gaussian mixture, that can express complex irradiance fields with a
small number of parameters. Our framework learns the distribution in a
progressive manner and does not need any warm-up phases. Due to the compact and
expressive representation of our density model, our framework can be
implemented entirely on the GPU, allowing it produce high quality images with
limited computational resources
Introduction: Ways of Machine Seeing
How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives.
This 'editorial' is for a special issue of AI & Society, which includes contributions from: MarĂa JesĂşs Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chávez Heras, Vladan Joler, Nicolas MalevĂ©, Lev Manovich, Nicholas Mirzoeff, Perle Møhl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen