Impact Statement:3-D view reconstruction has been one of the main fields of research and development ever since several applications, including autonomous navigation, robot-centric tasks,...Show More
Abstract:
Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, ...Show MoreMetadata
Impact Statement:
3-D view reconstruction has been one of the main fields of research and development ever since several applications, including autonomous navigation, robot-centric tasks, etc., have come into play. The significance of 3-D view reconstruction lies in contributing to increasing the accuracy, precision, and generation of 3-D views of 2-D objects and advancement in applications. The article critically explores the recent methods of real-time analysis on several benchmarked datasets. It offers insights into their potential contributions across diverse applications within the realm of 3-D view reconstruction.
Abstract:
Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library 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 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms.
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 5, Issue: 12, December 2024)