Pipeline for Data Collection and Real-Time Inference for Semantic Segmentation of 3D Point Clouds

• João Victor C. de Figueiredo UFG
• Rogério Albert M. P. Mozer UFG
• Victor M. Silva Souza UFG
• Analucia S. Morales UFSC
• Iwens G. Sene UFG
• Lucas Araújo Pereira UFG

Resumo

Semantic segmentation of 3D point clouds plays a fundamental role in applications such as autonomous vehicles, robotics, and 3D mapping, enabling the precise identification of objects and relevant classes. However, performing this task in real time presents challenges related to computational efficiency and model optimization. This work presents the development and performance analysis of a pipeline for real-time Light Detection and Ranging (LiDAR ) data acquisition and inference, integrating machine learning models based on Convolutional Neural Networks (CNNs). The proposed approach combines Software-in-the-Loop techniques and the concept of a digital twin, covering the entire process from data capture and preprocessing to semantic segmentation, ensuring a continuous processing workflow. The experiments done with an adaptation of the RIU-Net architecture demonstrated the feasibility of the proposed solution, showing satisfactory results in terms of execution time, accuracy, and Intersection over Union (IoU), reinforcing its applicability to real-time 3D computer vision systems.

Palavras-chave: Point cloud compression, Adaptation models, Three-dimensional displays, Laser radar, Semantic segmentation, Pipelines, Noise, Real-time systems, Data models, Autonomous vehicles, component, formatting, style, styling, insert