Generalizable 3D

Generalizable 3D reconstruction aims to create accurate and complete 3D models from limited input, such as a single image or a sparse set of views, adapting well to unseen scenes and objects. Current research heavily utilizes neural networks, particularly those based on implicit representations like neural radiance fields (NeRFs) and Gaussian splatting, along with techniques like self-supervision and divide-and-conquer strategies to improve generalization and efficiency. These advancements are significant for various applications, including augmented and virtual reality, robotics, and autonomous navigation, by enabling more robust and versatile 3D scene understanding.