An Extensible Plugin for Integrating High-Performance Collision Detection Broad-Phase Algorithms into Unity

This paper presents a plugin architecture for Unity to integrate and benchmark broad-phase collision detection algorithms. The solution connects high-performance C++ algorithms to Unity’s C# environment via an external dynamic-link library, allowing developers to test and compare different methods in interactive scenarios. We evaluated three integrated algorithms (Brute Force, Tracy, and KDTree) in tests with up to 1600 objects (uniform and assorted) across ten distinct simulation configurations, assessing both algorithmic performance and the integration bridge’s efficiency. Results show a stable architecture, validating the use of C++ DLLs for intensive tasks in Unity and providing a modular framework to extend its native physics capabilities.

Authors

Publication Details

Published
2025-09-18
DOI
https://doi.org/10.5753/se4games.2025.14883
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Extensible Plugin for Integrating High-Performance Collision Detection Broad-Phase Algorithms into Unity

Raul Queiroz Feitosa, Maria Andréia Formico Rodriguês
Advanced Neural Network Applications
article

An Extensible Plugin for Integrating High-Performance Collision Detection Broad-Phase Algorithms into Unity

Raul Queiroz Feitosa, Maria Andréia Formico Rodriguês
article en

Abstract

This paper presents a plugin architecture for Unity to integrate and benchmark broad-phase collision detection algorithms. The solution connects high-performance C++ algorithms to Unity’s C# environment via an external dynamic-link library, allowing developers to test and compare different methods in interactive scenarios. We evaluated three integrated algorithms (Brute Force, Tracy, and KDTree) in tests with up to 1600 objects (uniform and assorted) across ten distinct simulation configurations, assessing both algorithmic performance and the integration bridge’s efficiency. Results show a stable architecture, validating the use of C++ DLLs for intensive tasks in Unity and providing a modular framework to extend its native physics capabilities.

Openalex Percentile: Top 75%
Advanced Neural Network Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.