Refined 3D Modeling of Complex Models Based on Stereo Vision

Abstract

Despite the decreasing costs of stereo vision technologies, traditional 3D reconstruction methods still face challenges related to operational complexity and high costs, which somewhat limit their widespread adoption in various technical applications. Addressing these challenges, this paper presents an improved 3D reconstruction method combining COLMAP and OpenMVS, particularly suited for use with standard consumer-grade imaging devices such as smartphones and drones. By employing Structure from Motion (SFM) and Multi-View Stereo (MVS) techniques, this study significantly enhances image processing speed and achieves substantial improvements in model accuracy and detail reproduction. Systematic experimental validation has demonstrated that the combination of COLMAP and OpenMVS outperforms other open-source tools and combinations in terms of reconstruction speed and precision. This finding highlights the extensive potential and applicability of this combined approach in applications such as virtual reality, robotic navigation, and the digitization of cultural heritage.