A Comprehensive Exploration of Real-Time 3D View Reconstruction Methods

Real-time 3D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This paper thoroughly examines and tests contemporary methodologies addressing challenges in 3D view reconstruction. The methods being explored in this paper are categorized into volumetric and mesh, generative adversarial network based, and open source libraries based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3D view reconstruction in domains like robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The paper also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms.