Single View

Single-view 3D reconstruction aims to create complete three-dimensional models from a single two-dimensional image, a challenging inverse problem with significant implications for various fields. Current research heavily utilizes deep learning, focusing on diffusion models, neural radiance fields (NeRFs), and transformer architectures to address the inherent ambiguity of single-view data. These models often incorporate techniques like multi-view consistency enforcement, geometric constraints, and feature distillation from pre-trained models to improve accuracy and robustness. Advances in this area have broad applications, impacting fields such as autonomous driving, robotics, augmented reality, and 3D modeling.