Graphics Gems III (IBM Version) 1992
DOI: 10.1016/b978-0-08-050755-2.50034-8
|View full text |Cite
|
Sign up to set email alerts
|

Fast Random Rotation Matrices

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
82
0

Year Published

2005
2005
2024
2024

Publication Types

Select...
4
2
2

Relationship

0
8

Authors

Journals

citations
Cited by 83 publications
(82 citation statements)
references
References 0 publications
0
82
0
Order By: Relevance
“…• , since observations could coincide with any point on the precession period. The 3D orientation of the circumbinary system on the sky, with respect to a given observer, was randomised by applying a uniform 3D rotation algorithm by Arvo (1992). We simulated 10 000 randomly drawn systems over four years, and recorded stellar eclipses and planet transits.…”
Section: Impact Of the Observing Timespanmentioning
confidence: 99%
“…• , since observations could coincide with any point on the precession period. The 3D orientation of the circumbinary system on the sky, with respect to a given observer, was randomised by applying a uniform 3D rotation algorithm by Arvo (1992). We simulated 10 000 randomly drawn systems over four years, and recorded stellar eclipses and planet transits.…”
Section: Impact Of the Observing Timespanmentioning
confidence: 99%
“…To generate the poses, we assume no prior information on the rotation and so we generate random rotation matrices according to [Arvo, 1992]. However, we initialise the translation such that the mean of the 3D data is in the centre of the image (which is the case in the synthetic data, and is sufficiently close for the real data) and initialise a random depth within 5% from the ground truth.…”
Section: Methodsmentioning
confidence: 99%
“…The synthetic datasets are the Stanford bunny and Stanford dragon, and the horse and angel from the Large Geometric Models Archive from Georgia Institute of Technology. For the synthetic datasets, textureless images were generated using POV-Ray from a random angle [Arvo, 1992] and fixed translation, using a point light source at the same location as the camera. The two real datasets are the dinosaur and temple from Middlebury's multi-view reconstruction dataset [Seitz et al, 2006].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Our first approach was to use evenly distributed sets of the three rotational angles, however, this leads to a very uneven sampling and is highly inefficient since some areas are heavily oversampled. We therefore used a simple, more promising approach, random sample generation [7]. Deterministic sample generation with good properties is challenging and application of existing results could be a topic of further research.…”
Section: Introductionmentioning
confidence: 99%