Empirical Studies of Large Scale Environment Scanning by Consumer Electronics

TL;DR

This study empirically evaluates the Matterport Pro3's effectiveness for large-scale environment reconstruction, demonstrating its high-quality 3D modeling capabilities and comparing it with consumer devices like the iPhone.

Contribution

It provides a comprehensive empirical assessment of the Matterport Pro3's performance in large-scale scanning and introduces solutions to address encountered challenges.

Findings

Pro3 produces denser point clouds than iPhone.

Pro3 achieves higher alignment accuracy with RMSE of 0.0118 meters.

Pro3 effectively captures large-scale environments for practical applications.

Abstract

This paper presents an empirical evaluation of the Matterport Pro3, a consumer-grade 3D scanning device, for large-scale environment reconstruction. We conduct detailed scanning (1,099 scanning points) of a six-floor building (17,567 square meters) and assess the device's effectiveness, limitations, and performance enhancements in diverse scenarios. Challenges encountered during the scanning are addressed through proposed solutions, while we also explore advanced methods to overcome them more effectively. Comparative analysis with another consumer-grade device (iPhone) highlights the Pro3's balance between cost-effectiveness and performance. The Matterport Pro3 achieves a denser point cloud with 1,877,324 points compared to the iPhone's 506,961 points and higher alignment accuracy with an RMSE of 0.0118 meters. The cloud-to-cloud (C2C) average distance error between the two point cloud models is 0.0408 meters, with a standard deviation of 0.0715 meters. The study demonstrates the Pro3's ability to generate high-quality 3D models suitable for large-scale applications, leveraging features such as LiDAR and advanced alignment techniques.