Recent Advances in Real-Time Collision and Proximity Computations for Games and Simulations
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".