Analysis of latency reduction and performance improvement methods in selected VR applications
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Issue Vol. 38 (2026)
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Comparative analysis of Next.js and Astro frameworks
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Behavioral analysis of ransomware threats to ESXi Hypervisors: a machine learning-based predictive model
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Performance and usability evaluation of a VR virtual museum application
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Comparative analysis of selected data visualization methods
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A performance comparison of web programming interfaces: GraphQL, gRPC and Thrift
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A review of security mechanisms in electronic payment systems
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Clustering methods in machine learning
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Comparative analysis of interpretable artificial intelligence methods
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Comparative analysis of machine learning classifiers
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66-72
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Comparative analysis of the functionalities of applications supporting the self-control process of anticoagulation therapy
73-80
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Application of machine learning for predicting Formula 1 race results
81-86
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Analysis of latency reduction and performance improvement methods in selected VR applications
87-94
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Integrating deep learning image analysis into Web GIS applications: A Hybrid Flask - Spring Boot architecture for automated place detection
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Abstract
The aim of the work was to study the impact of two chosen optimization methods on the performance of selected VR applications. Four versions of two Unity applications have been tested, differing by the rendering mode used and the use of multithreaded rendering, and their performance metrics have been compared. Testing was performed using the Meta Quest Pro VR headset. The best overall metrics have been observed for the configuration combining multithreaded rendering with multipass rendering, which achieved a frame rate higher by over 5 frames per second compared to other configurations for one of the applications, and the lowest application GPU time for both applications. However, the use of multiview rendering led to a reduction in average CPU utilization of 6.83 to 9.32 percentage points.
