Método Rápido para Codificação de Nuvens de Pontos Dinâmicas Baseado em Predição de Particionamento de Blocos - Rehbein, Gustavo; Corrêa, Guilherme; Santos, Cristiano; Porto, Marcelo (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15951, 2025)
Point clouds are a versatile 3D data representation format that captures spatial and attribute information about objects or environments. It has been widely used in applications like autonomous driving, augmented and virtual reality, and 3D scanning. However, the large volume of data involved in point cloud processing poses challenges in terms of storage, transmission, and real-time processing. The Video-based Point Cloud Compression (V-PCC) standard addresses these challenges by employing 2D video compression techniques to encode dynamic 3D point clouds. Despite its effectiveness, V-PCC demands a high computational cost, particularly in encoding geometry and attribute video sub-streams. This paper presents a machine learning-based approach to accelerate the V-PCC encoding, focusing on the Coding Unit partitioning process in the geometry and attribute sub-streams deployed in the reference software (TMC2). The proposed method significantly reduces the encoding complexity by using decision tree models with negligible impact on coding efficiency. Experimental results demonstrate an average encoding time reduction of 36.12% for geometry and attribute sub-streams, with a minimal impact on coding efficiency between -0.43% and 0.83% in terms of BD-Rate.