Single View 3D Reconstruction

Single-view 3D reconstruction aims to create three-dimensional models from a single two-dimensional image, a fundamentally challenging inverse problem due to inherent ambiguities. Current research focuses on improving accuracy and efficiency through novel neural network architectures, such as those employing Gaussian splatting, transformers, and mesh deformation techniques, often incorporating physical constraints or multi-view consistency for enhanced realism. These advancements are significant for applications ranging from virtual try-ons and robotic manipulation to 3D modeling and scene understanding, pushing the boundaries of what's possible with limited visual input.