Efficient and accurate 3D reconstruction is crucial for various applications,
including augmented and virtual reality, medical imaging, and cinematic special
effects. While traditional Multi-View Stereo (MVS) systems have been
fundamental in these applications, using neural implicit fields in implicit 3D
scene modeling has introduced new possibilities for handling complex topologies
and continuous surfaces. However, neural implicit fields often suffer from
computational inefficiencies, overfitting, and heavy reliance on data quality,
limiting their practical use. This paper presents an enhanced MVS framework
that integrates multi-view 360-degree imagery with robust camera pose
estimation via Structure from Motion (SfM) and advanced image processing for
point cloud densification, mesh reconstruction, and texturing. Our approach
significantly improves upon traditional MVS methods, offering superior accuracy
and precision as validated using Chamfer distance metrics on the Realistic
Synthetic 360 dataset. The developed MVS technique enhances the detail and
clarity of 3D reconstructions and demonstrates superior computational
efficiency and robustness in complex scene reconstruction, effectively handling
occlusions and varying viewpoints. These improvements suggest that our MVS
framework can compete with and potentially exceed current state-of-the-art
neural implicit field methods, especially in scenarios requiring real-time
processing and scalability.

通过结合多视角全景图像、鲁棒的相机位姿估计、先进的图像处理以及点云密度化、网格重建和纹理生成等技术，本论文提出了一个增强型的多视角立体重建框架，显著提高了传统方法的准确性和精度，在处理遮挡和视角变化的复杂场景重建方面表现出了优异的计算效率和鲁棒性，对于实时处理和可扩展性要求高的情况，可以与当前最先进的神经隐式场方法相竞争甚至超越。