Single View Reconstruction

Single-view reconstruction aims to create a complete 3D model from a single 2D image, a challenging inverse problem in computer vision. Current research focuses on leveraging deep learning, particularly diffusion models and neural radiance fields (NeRFs), often incorporating physics-based constraints or multi-view consistency techniques to improve accuracy and handle occlusions. These advancements are improving the speed and quality of 3D model generation, with applications ranging from robotics and augmented reality to biomedical imaging and digital content creation. The development of efficient and generalizable methods remains a key focus.