Real-time 3D object reconstruction has gained significant attention in computer vision and graphics research due to its applications in augmented reality, robotics, and virtual reality. Traditional methods for 3D reconstruction often rely on time-consuming processes and lack real-time capabilities. In recent years, Multi-View Stereo (MVS) Networks have emerged as a promising approach to tackle real-time 3D object reconstruction challenges. The advantages of using MVS Networks for real-time 3D object reconstruction include their ability to handle complex scenes, handle occlusions, and generate accurate depth maps. The use of deep learning techniques enables efficient and parallel processing, facilitating real-time reconstruction even on resource-constrained devices. Overall, real-time 3D object reconstruction using Multi-View Stereo (MVS) Networks shows great potential in enabling interactive and immersive experiences in applications such as virtual reality, augmented reality, and robotics, with the ability to reconstruct 3D objects in real-time from multiple viewpoints.