Analysis and Synthesis of Digital Dyadic Sequences
June 12, 2023 Β· Declared Dead Β· π ACM Transactions on Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Abdalla G. M. Ahmed, Mikhail Skopenkov, Markus Hadwiger, Peter Wonka
arXiv ID
2306.06925
Category
cs.GR: Graphics
Cross-listed
math.CO
Citations
18
Venue
ACM Transactions on Graphics
Last Checked
5 months ago
Abstract
We explore the space of matrix-generated (0, m, 2)-nets and (0, 2)-sequences in base 2, also known as digital dyadic nets and sequences. In computer graphics, they are arguably leading the competition for use in rendering. We provide a complete characterization of the design space and count the possible number of constructions with and without considering possible reorderings of the point set. Based on this analysis, we then show that every digital dyadic net can be reordered into a sequence, together with a corresponding algorithm. Finally, we present a novel family of self-similar digital dyadic sequences, to be named $ΞΎ$-sequences, that spans a subspace with fewer degrees of freedom. Those $ΞΎ$-sequences are extremely efficient to sample and compute, and we demonstrate their advantages over the classic Sobol (0, 2)-sequence.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Graphics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Deep Bilateral Learning for Real-Time Image Enhancement
R.I.P.
π»
Ghosted
Animating Human Athletics
R.I.P.
π»
Ghosted
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
R.I.P.
π»
Ghosted
Shape Transformation Using Variational Implicit Functions
R.I.P.
π»
Ghosted
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted