Empirical Studies of Large Scale Environment Scanning by Consumer Electronics
This study addresses the practical challenge of cost-effective large-scale 3D scanning for consumer electronics applications, though it is incremental in comparing existing devices.
This paper empirically evaluates the Matterport Pro3 consumer-grade 3D scanner for large-scale environment reconstruction, finding it produces denser point clouds (1,877,324 vs. 506,961 points) and higher alignment accuracy (RMSE 0.0118 meters) compared to an iPhone scanner.
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.