Computational Cost Reduction in Dynamic Point Cloud Encoding

• Gustavo Rehbein UFPel
• Guilherme Corrêa UFPel
• Cristiano Santos UFPel
• Marcelo Porto UFPel

DOI: https://doi.org/10.5753/webmedia_estendido.2025.16425

Abstract

This dissertation presents a machine learning-based solution to reduce the computational cost of compressing dynamic point clouds under the V-PCC standard. The proposed method employs decision models to accelerate the video encoding stage, which accounts for most of the processing. Results show an average 60% reduction in encoding time in Random Access mode, with minimal impact on coding efficiency (approximately 1.3% increase in BD-Rate), making the solution suitable for real-time applications and resource-constrained devices.

Keywords: Compressão de Nuvens de Pontos, Aprendizado de Máquina, Redução de Custo Computacional

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