Broadmark: A Testing Framework for Broad‐Phase Collision Detection Algorithms

Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground...

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Veröffentlicht in:	Computer graphics forum Jg. 39; H. 1; S. 436 - 449
Hauptverfasser:	Serpa, Ygor Rebouças, Rodrigues, Maria Andréia Formico
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.02.2020
Schlagworte:	Algorithms Benchmarks broad phase Categories and Subject Descriptors (according to ACM CCS): Computer Graphics [Computing Methodologies]: Animation–Collision detection collision detection Computer simulation CPU and GPU algorithms open‐source framework state‐of‐the‐art implementations
ISSN:	0167-7055, 1467-8659
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Abstract	Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions. Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions.
AbstractList	Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions. Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions. Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions.
Author	Rodrigues, Maria Andréia Formico Serpa, Ygor Rebouças
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SubjectTerms	Algorithms Benchmarks broad phase Categories and Subject Descriptors (according to ACM CCS): Computer Graphics [Computing Methodologies]: Animation–Collision detection collision detection Computer simulation CPU and GPU algorithms open‐source framework state‐of‐the‐art implementations
Title	Broadmark: A Testing Framework for Broad‐Phase Collision Detection Algorithms
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