Recent Advances in Real-Time Collision and Proximity Computations for Games and Simulations
Notice bibliographique
Résumé
This course is intended for instructing students and practitioners on recent developments related to collision and proximity computations for interactive games and simulations. There have been significant advances in various physics-based simulation techniques for movies, interactive games, and virtual environments. Most recent work has been on achieving realistic simulations of rigid, articulated, deforming, and fracturing models. However, many complex and challenging simulations (e.g., fracturing simulation) are not widely used in interactive games because of their computational requirements, although the hardware capability of current CPUs and GPUs has considerably improving. It is well known that one of the main performance bottlenecks in most simulations lies in proximity queries including collision detection, minimum separation distance, and penetration depth computations. As a result, there has been significant recent research on developing real-time proximity computation algorithms for interactive games and high-quality simulations. Some of recent advanced techniques are able to achieve interactive performance even for most challenging simulations such as fracturing or large-scale cloth simulations. However, these techniques are quite complicated. Moreover, they require in-depth geometric background and sophisticated optimizations on multi-core architectures. These techniques, therefore, have not been easily accessible to students and practitioners who work on real-time simulation methods. Our objective is to introduce and teach students and practitioners about efficient proximity computation methods and their practical implementations. By doing so, we can expose the attendees to the latest developments, to bridge the gap between the two different fields: proximity computation and simulation. At a broad level, this course will cover the following topics: Basic algorithms for various proximity queries including collision detection, minimum separation distances, penetration depth, etc.; Discrete and continuous algorithms for rigid, articulated, deforming, and fracturing models. Parallel algorithms that utilize many cores of CPUs, GPUs, or CPUs/GPUs. Applications of various proximity queries in Havoc, a widely used Physics simulation package. Optimized proximity data structures for many-core architectures including GPU. Integrating proximity computation algorithms into physically-based simulation systems. We have four instructors from academia and industry, each of who has significant experiences in designing and implementing different aspects of the aforementioned teaching materials. Since each instructor is a world-class expert in his field, students will receive the best instruction. Moreover, students and practitioners can learn how the industry-leading physics systems benefits from efficient proximity queries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».