New geometric algorithms and data structures for collision detection of dynamically deforming objects

Any virtual environment that supports interactions between virtual objects and/or a user and objects, needs a collision detection system to handle all interactions in a physically correct or plausible way. A collision detection system is needed to determine if objects are in contact or interpenetrates. These interpenetrations are resolved by a collision handling system. Because of the fact, that in nearly all simulations objects can interact with each other, collision detection is a fundamental technology, that is needed in all these simulations, like physically based simulation, robotic path and motion planning, virtual prototyping, and many more. Most virtual environments aim to represent the real-world as realistic as possible and therefore, virtual environments getting more and more complex. Furthermore, all models in a virtual environment should interact like real objects do, if forces are applied to the objects. Nearly all real-world objects will deform or break down in its individual parts if forces are acted upon the objects. Thus deformable objects are becoming more and more common in virtual environments, which want to be as realistic as possible and thus, will present new challenges to the collision detection system. The necessary collision detection computations can be very complex and this has the effect, that the collision detection process is the performance bottleneck in most simulations. Most rigid body collision detection approaches use a BVH as acceleration data structure. This technique is perfectly suitable if the object does not change its shape. For a soft body an update step is necessary to ensure that the underlying acceleration data structure is still valid after performing a simulation step. This update step can be very time consuming, is often hard to implement and in most cases will produce a degenerated BVH after some simulation steps, if the objects generally deform. Therefore, the here presented collision detection approach works entirely without an acceleration data structure and supports rigid and soft bodies. Furthermore, we can compute inter-object and intraobject collisions of rigid and deformable objects consisting of many tens of thousands of triangles in a few milliseconds. To realize this, a subdivision of the scene into parts using a fuzzy clustering approach is applied. Based on that all further steps for each cluster can be performed in parallel and if desired, distributed to different GPUs. Tests have been performed to judge the performance of our approach against other state-of-the-art collision detection algorithms. Additionally, we integrated our approach into Bullet, a commonly used physics engine, to evaluate our algorithm. In order to make a fair comparison of different rigid body collision detection algorithms, we propose a new collision detection Benchmarking Suite. Our Benchmarking Suite can evaluate both the performance as well as the quality of the collision response. Therefore, the Benchmarking Suite is subdivided into a Performance Benchmark and a Quality Benchmark. This approach needs to be extended to support soft body collision detection algorithms in the future.Jede virtuelle Umgebung, welche eine Interaktion zwischen den virtuellen Objekten in der Szene zulässt und/oder zwischen einem Benutzer und den Objekten, benötigt für eine korrekte Behandlung der Interaktionen eine Kollisionsdetektion. Nur dank der Kollisionsdetektion können Berührungen zwischen Objekten erkannt und mittels der Kollisionsbehandlung aufgelöst werden. Dies ist der Grund für die weite Verbreitung der Kollisionsdetektion in die verschiedensten Fachbereiche, wie der physikalisch basierten Simulation, der Pfadplanung in der Robotik, dem virtuellen Prototyping und vielen weiteren. Auf Grund des Bestrebens, die reale Umgebung in der virtuellen Welt so realistisch wie möglich nachzubilden, steigt die Komplexität der Szenen stetig. Fortwährend steigen die Anforderungen an die Objekte, sich realistisch zu verhalten, sollten Kräfte auf die einzelnen Objekte ausgeübt werden. Die meisten Objekte, die uns in unserer realen Welt umgeben, ändern ihre Form oder zerbrechen in ihre Einzelteile, wenn Kräfte auf sie einwirken. Daher kommen in realitätsnahen, virtuellen Umgebungen immer häufiger deformierbare Objekte zum Einsatz, was neue Herausforderungen an die Kollisionsdetektion stellt. Die hierfür Notwendigen, teils komplexen Berechnungen, führen dazu, dass die Kollisionsdetektion häufig der Performance-Bottleneck in der jeweiligen Simulation darstellt. Die meisten Kollisionsdetektionen für starre Körper benutzen eine Hüllkörperhierarchie als Beschleunigungsdatenstruktur. Diese Technik ist hervorragend geeignet, solange sich die Form des Objektes nicht verändert. Im Fall von deformierbaren Objekten ist eine Aktualisierung der Datenstruktur nach jedem Schritt der Simulation notwendig, damit diese weiterhin gültig ist. Dieser Aktualisierungsschritt kann, je nach Hierarchie, sehr zeitaufwendig sein, ist in den meisten Fällen schwer zu implementieren und generiert nach vielen Schritten der Simulation häufig eine entartete Hüllkörperhierarchie, sollte sich das Objekt sehr stark verformen. Um dies zu vermeiden, verzichtet unsere Kollisionsdetektion vollständig auf eine Beschleunigungsdatenstruktur und unterstützt sowohl rigide, wie auch deformierbare Körper. Zugleich können wir Selbstkollisionen und Kollisionen zwischen starren und/oder deformierbaren Objekten, bestehend aus vielen Zehntausenden Dreiecken, innerhalb von wenigen Millisekunden berechnen. Um dies zu realisieren, unterteilen wir die gesamte Szene in einzelne Bereiche mittels eines Fuzzy Clustering-Verfahrens. Dies ermöglicht es, dass alle Cluster unabhängig bearbeitet werden und falls gewünscht, die Berechnungen für die einzelnen Cluster auf verschiedene Grafikkarten verteilt werden können. Um die Leistungsfähigkeit unseres Ansatzes vergleichen zu können, haben wir diesen gegen aktuelle Verfahren für die Kollisionsdetektion antreten lassen. Weiterhin haben wir unser Verfahren in die Physik-Engine Bullet integriert, um das Verhalten in dynamischen Situationen zu evaluieren. Um unterschiedliche Kollisionsdetektionsalgorithmen für starre Körper korrekt und objektiv miteinander vergleichen zu können, haben wir eine Benchmarking-Suite entwickelt. Unsere Benchmarking- Suite kann sowohl die Geschwindigkeit, für die Bestimmung, ob zwei Objekte sich durchdringen, wie auch die Qualität der berechneten Kräfte miteinander vergleichen. Hierfür ist die Benchmarking-Suite in den Performance Benchmark und den Quality Benchmark unterteilt worden. In der Zukunft wird diese Benchmarking-Suite dahingehend erweitert, dass auch Kollisionsdetektionsalgorithmen für deformierbare Objekte unterstützt werden

WISE/NEOWISE Preliminary Analysis and Highlights of the 67P/Churyumov-Gerasimenko Near Nucleus Environs

On January 18-19 and June 28-29 of 2010, the Wide-field Infrared Survey Explorer (WISE) spacecraft imaged the Rosetta mission target, comet 67P/Churyumov-Gerasimenko. We present a preliminary analysis of the images, which provide a characterization of the dust environment at heliocentric distances similar to those planned for the initial spacecraft encounter, but on the outbound leg of its orbit rather than the inbound. Broad-band photometry yields low levels of CO2 production at a comet heliocentric distance of 3.32 AU and no detectable production at 4.18 AU. We find that at these heliocentric distances, large dust grains with mean grain diameters on the order of a millimeter or greater dominate the coma and evolve to populate the tail. This is further supported by broad-band photometry centered on the nucleus, which yield an estimated differential dust particle size distribution with a power law relation that is considerably shallower than average. We set a 3-sigma upper limit constraint on the albedo of the large-grain dust at <= 0.12. Our best estimate of the nucleus radius (1.82 +/- 0.20 km) and albedo (0.04 +/- 0.01) are in agreement with measurements previously reported in the literature

WISE/NEOWISE observations of Active Bodies in the Main Belt

We report results based on mid-infrared photometry of 5 active main belt objects (AMBOs) detected by the Wide-field Infrared Survey Explorer (WISE) spacecraft. Four of these bodies, P/2010 R2 (La Sagra), 133P/Elst-Pizarro, (596) Scheila, and 176P/LINEAR, showed no signs of activity at the time of the observations, allowing the WISE detections to place firm constraints on their diameters and albedos. Geometric albedos were in the range of a few percent, and on the order of other measured comet nuclei. P/2010 A2 was observed on April 2-3, 2010, three months after its peak activity. Photometry of the coma at 12 and 22 {\mu}m combined with ground-based visible-wavelength measurements provides constraints on the dust particle mass distribution (PMD), dlogn/dlogm, yielding power-law slope values of {\alpha} = -0.5 +/- 0.1. This PMD is considerably more shallow than that found for other comets, in particular inbound particle fluence during the Stardust encounter of comet 81P/Wild 2. It is similar to the PMD seen for 9P/Tempel 1 in the immediate aftermath of the Deep Impact experiment. Upper limits for CO2 & CO production are also provided for each AMBO and compared with revised production numbers for WISE observations of 103P/Hartley 2.Comment: 32 Pages, including 5 Figure

WISE/NEOWISE Observations of Comet 103P/Hartley 2

We report results based on mid-infrared photometry of comet 103P/Hartley 2 taken during 2010 May 4-13 (when the comet was at a heliocentric distance of 2.3 AU, and an observer distance of 2.0 AU) by the Wide-field Infrared Survey Explorer. Photometry of the coma at 22 μm and data from the University of Hawaii 2.2 m telescope obtained on 2010 May 22 provide constraints on the dust particle size distribution, d log n/d log m, yielding power-law slope values of alpha = –0.97 ± 0.10, steeper than that found for the inbound particle fluence during the Stardust encounter of comet 81P/Wild 2. The extracted nucleus signal at 12 μm is consistent with a body of average spherical radius of 0.6 ± 0.2 km (one standard deviation), assuming a beaming parameter of 1.2. The 4.6 μm band signal in excess of dust and nucleus reflected and thermal contributions may be attributed to carbon monoxide or carbon dioxide emission lines and provides limits and estimates of species production. Derived carbon dioxide coma production rates are 3.5(± 0.9) × 10^(24) molecules per second. Analyses of the trail signal present in the stacked image with an effective exposure time of 158.4 s yields optical-depth values near 9 × 10^(–10) at a delta mean anomaly of 0.2 deg trailing the comet nucleus, in both 12 and 22 μm bands. A minimum chi-squared analysis of the dust trail position yields a beta-parameter value of 1.0 × 10^(–4), consistent with a derived mean trail-grain diameter of 1.1/ρ cm for grains of ρ g cm^(–3) density. This leads to a total detected trail mass of at least 4 × 10^(10) ρ kg

The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance

The all sky surveys done by the Palomar Observatory Schmidt, the European Southern Observatory Schmidt, and the United Kingdom Schmidt, the InfraRed Astronomical Satellite and the 2 Micron All Sky Survey have proven to be extremely useful tools for astronomy with value that lasts for decades. The Wide-field Infrared Survey Explorer is mapping the whole sky following its launch on 14 December 2009. WISE began surveying the sky on 14 Jan 2010 and completed its first full coverage of the sky on July 17. The survey will continue to cover the sky a second time until the cryogen is exhausted (anticipated in November 2010). WISE is achieving 5 sigma point source sensitivities better than 0.08, 0.11, 1 and 6 mJy in unconfused regions on the ecliptic in bands centered at wavelengths of 3.4, 4.6, 12 and 22 microns. Sensitivity improves toward the ecliptic poles due to denser coverage and lower zodiacal background. The angular resolution is 6.1, 6.4, 6.5 and 12.0 arc-seconds at 3.4, 4.6, 12 and 22 microns, and the astrometric precision for high SNR sources is better than 0.15 arc-seconds.Comment: 22 pages with 19 included figures. Updated to better match the accepted version in the A

Ultracool Field Brown Dwarf Candidates Selected at 4.5 microns

We have identified a sample of cool field brown dwarf candidates using IRAC data from the Spitzer Deep, Wide-Field Survey (SDWFS). The candidates were selected from 400,000 SDWFS sources with [4.5] <= 18.5 mag and required to have [3.6]-[4.5] >= 1.5 and [4.5] - [8.0] <= 2.0 on the Vega system. The first color requirement selects objects redder than all but a handful of presently known brown dwarfs with spectral classes later than T7, while the second eliminates 14 probable reddened AGN. Optical detection of 4 of the remaining 18 sources implies they are likely also AGN, leaving 14 brown dwarf candidates. For two of the brightest candidates (SDWFS J143524.44+335334.6 and SDWFS J143222.82+323746.5), the spectral energy distributions including near-infrared detections suggest a spectral class of ~ T8. The proper motion is < 0.25 "/yr, consistent with expectations for a luminosity inferred distance of >70 pc. The reddest brown dwarf candidate (SDWFS J143356.62+351849.2) has [3.6] - [4.5]=2.24 and H - [4.5] > 5.7, redder than any published brown dwarf in these colors, and may be the first example of the elusive Y-dwarf spectral class. Models from Burrows et al. (2003) predict larger numbers of cool brown dwarfs should be found for a Chabrier (2003) mass function. Suppressing the model [4.5] flux by a factor of two, as indicated by previous work, brings the Burrows models and observations into reasonable agreement. The recently launched Wide-field Infrared Survey Explorer (WISE) will probe a volume ~40x larger and should find hundreds of brown dwarfs cooler than T7.Comment: 13 pages, 6 figures, accepted for publication in the June 2010 issue of The Astronomical Journa

The state of the focus and image quality of the Spitzer Space Telescope as measured in orbit

We describe the process by which the NASA Spitzer Space Telescope (SST) Cryogenic Telescope Assembly (CTA) was brought into focus after arrival of the spacecraft in orbit. The ground rules of the mission did not allow us to make a conventional focus sweep. A strategy was developed to determine the focus position through a program of passive imaging during the observatory cool-down time period. A number of analytical diagnostic tools were developed to facilitate evaluation of the state of the CTA focus. Initially, these tools were used to establish the in-orbit focus position. These tools were then used to evaluate the effects of an initial small exploratory move that verified the health and calibration of the secondary mirror focus mechanism. A second large move of the secondary mirror was then commanded to bring the telescope into focus. We present images that show the CTA Point Spread Function (PSF) at different channel wavelengths and demonstrate that the telescope achieved diffraction limited performance at a wavelength of 5.5 μm, somewhat better than the level-one requirement

The CatWISE Preliminary Catalog: Motions from WISE{\it WISE} and NEOWISE{\it NEOWISE} Data

CatWISE is a program to catalog sources selected from combined ${\it WISE}$ and ${\it NEOWISE}$ all-sky survey data at 3.4 and 4.6 $\mu$m (W1 and W2). The CatWISE Preliminary Catalog consists of 900,849,014 sources measured in data collected from 2010 to 2016. This dataset represents four times as many exposures and spans over ten times as large a time baseline as that used for the AllWISE Catalog. CatWISE adapts AllWISE software to measure the sources in coadded images created from six-month subsets of these data, each representing one coverage of the inertial sky, or epoch. The catalog includes the measured motion of sources in 8 epochs over the 6.5 year span of the data. From comparison to ${\it Spitzer}$, the SNR=5 limits in magnitudes in the Vega system are W1=17.67 and W2=16.47, compared to W1=16.96 and W2=16.02 for AllWISE. From comparison to ${\it Gaia}$, CatWISE positions have typical accuracies of 50 mas for stars at W1=10 mag and 275 mas for stars at W1=15.5 mag. Proper motions have typical accuracies of 10 mas yr$^{-1}$ and 30 mas yr$^{-1}$ for stars with these brightnesses, an order of magnitude better than from AllWISE. The catalog is available in the WISE/NEOWISE Enhanced and Contributed Products area of the NASA/IPAC Infrared Science Archive.Comment: 53 pages, 20 figures, 5 tables. Accepted by ApJ