  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.
