Real-Time Incremental Point Cloud Acquisition and Spatial Bounding Box Filtering Using Lidar Sensors in Virtual Reality Environments

This paper introduces a Unity-based Virtual Reality (VR) system designed for real-time, incremental acquisition, processing, and interactive spatial filtering of LiDAR-generated point clouds on the Meta Quest 3 platform. Using an Intel RealSense L515 sensor and SLAM-inspired alignment techniques, the system enables dynamic, user-defined spatial segmentation through bounding boxes to refine collected data. The proposed framework supports multi-pass acquisitions, merging, and cleaning of point clouds, effectively addressing the limitations of traditional batch processing workflows. Additionally, the system provides an efficient modular structure that integrates live visualization using the Pcx Unity asset and optimizes performance through binary PLY file conversion. A case study on capturing a Meta Quest 3 protective case demonstrates the system’s robustness and ability to preserve geometric fidelity across incremental scans, with minimal redundancy and automatic alignment. The results highlight the potential for future expansion into real-time volumetric reconstruction, automated point cloud registration, and advanced spatial computing applications. By combining modular software architecture with accessible hardware configurations, this work contributes a practical framework for immersive 3D data handling, aligning with ongoing research in real-time 3D reconstruction, interactive VR applications, and advanced point cloud processing.