VDB: High-resolution sparse volumes with dynamic topology
By Ken Museth. TOGS, 2013
432 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Signal Processing
View Paper: https://www.semanticscholar.org/paper/6fd2e71782b7408078dfc3bf7334a3553569f187
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Kyle Genova
- William T. Freeman
- Forrester Cole
- Aaron Sarna
- Jamie Shotton
- Matthew Johnson
- Thomas A. Funkhouser
- Daniel Vlasic
In Computer Graphics and Computer-Aided Design
- Yuanming Hu
- Jamie Shotton
- Matthew Johnson
- William T. Freeman
- Thomas A. Funkhouser
- Daniel Vlasic
- Chenfanfu Jiang
- Kyle Genova
In Signal Processing
- Jamie Shotton
- Matthew Johnson
- William T. Freeman
- Thomas A. Funkhouser
- Daniel Vlasic
- Kyle Genova
- Forrester Cole
- Julien P. C. Valentin
Citations in the corpus
- A moving least squares material point method with displacement discontinuity and two-way rigid body coupling (2018, background): “Traditional SDF level sets such as OpenVDB [Museth 2013] can be easily constructed from closed surfaces.”
- FastNeRF: High-Fidelity Neural Rendering at 200FPS (2021, methodology): “Once the cache is generated, we convert it to a sparse data structure using OpenVDB [26], which improves performance and reduces the memory footprint.”
- Learning Shape Templates With Structured Implicit Functions (2019, methodology): “In order to do the watertight conversion, we first convert the meshes to a 300 3 sparse voxel representation [37].”