用消费级扫描仪重建六层楼建筑，效果优于手机。
Empirical Studies of Large Scale Environment Scanning by Consumer Electronics
- 用Matterport Pro3在6层楼1.7万平米区域扫描1099个点。
- 点云密度达187万，对齐误差仅0.0118米，比iPhone高3倍。
- 适合建筑、地产等需要大范围高精度3D建模的场景。
本文对消费级3D扫描设备Matterport Pro3进行了大规模环境重建的实证评估。我们在一栋六层楼（17,567平方米）的建筑中进行了1,099个扫描点的详细扫描，全面评估了该设备在不同场景下的有效性、局限性及性能提升。通过提出解决方案应对扫描过程中遇到的挑战，并探索更高效的改进方法。与另一款消费级设备iPhone进行对比分析，结果表明Pro3在成本效益与性能之间取得良好平衡：其生成的点云密度达1,877,324点，远超iPhone的506,961点；对齐精度更高，均方根误差（RMSE）为0.0118米。两模型间的点云到点云（C2C）平均距离误差为0.0408米，标准差0.0715米。研究证明，Pro3凭借其激光雷达和先进对齐技术，能生成适用于大规模应用的高质量3D模型。
原文摘要 · Abstract (English)
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.
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